E-Model for Online Learning Communities.

Science.gov (United States)

Rogo, Ellen J; Portillo, Karen M

2015-10-01

The purpose of this study was to explore the students' perspectives on the phenomenon of online learning communities while enrolled in a graduate dental hygiene program. A qualitative case study method was designed to investigate the learners' experiences with communities in an online environment. A cross-sectional purposive sampling method was used. Interviews were the data collection method. As the original data were being analyzed, the researchers noted a pattern evolved indicating the phenomenon developed in stages. The data were re-analyzed and validated by 2 member checks. The participants' experiences revealed an e-model consisting of 3 stages of formal learning community development as core courses in the curriculum were completed and 1 stage related to transmuting the community to an informal entity as students experienced the independent coursework in the program. The development of the formal learning communities followed 3 stages: Building a Foundation for the Learning Community, Building a Supportive Network within the Learning Community and Investing in the Community to Enhance Learning. The last stage, Transforming the Learning Community, signaled a transition to an informal network of learners. The e-model was represented by 3 key elements: metamorphosis of relationships, metamorphosis through the affective domain and metamorphosis through the cognitive domain, with the most influential element being the affective development. The e-model describes a 4 stage process through which learners experience a metamorphosis in their affective, relationship and cognitive development. Synergistic learning was possible based on the interaction between synergistic relationships and affective actions. Copyright © 2015 The American Dental Hygienists’ Association.

Learning Latent Structure in Complex Networks

DEFF Research Database (Denmark)

Mørup, Morten; Hansen, Lars Kai

such as the Modularity, it has recently been shown that latent structure in complex networks is learnable by Bayesian generative link distribution models (Airoldi et al., 2008, Hofman and Wiggins, 2008). In this paper we propose a new generative model that allows representation of latent community structure......Latent structure in complex networks, e.g., in the form of community structure, can help understand network dynamics, identify heterogeneities in network properties, and predict ‘missing’ links. While most community detection algorithms are based on optimizing heuristic clustering objectives...... as in the previous Bayesian approaches and in addition allows learning of node specific link properties similar to that in the modularity objective. We employ a new relaxation method for efficient inference in these generative models that allows us to learn the behavior of very large networks. We compare the link...

Material matters for learning in virtual networks: a case study of a professional learning programme hosted in a Google+ online community

Directory of Open Access Journals (Sweden)

Aileen Ackland

2015-08-01

Full Text Available In this paper, we draw on Actor–Network Theories (ANT to explore how material components functioned to create gateways and barriers to a virtual learning network in the context of a professional development module in higher education. Students were practitioners engaged in family learning in different professional roles and contexts. The data comprised postings in the Google+ community, email correspondence, meeting notes, feedback submitted at the final workshop and post-module evaluation forms. Our analysis revealed a complex set of interactions, and suggests multiple ways human actors story their encounters with non-human components and the effects these have on the learning experience. The aim of this paper is to contribute to a more holistic understanding of the components and dynamics of social learning networks in the virtual world and consider the implications for the design of online learning for continuous professional development (CPD.

A Team Formation and Project-based Learning Support Service for Social Learning Networks

NARCIS (Netherlands)

Spoelstra, Howard; Van Rosmalen, Peter; Van de Vrie, Evert; Obreza, Matija; Sloep, Peter

2014-01-01

The Internet affords new approaches to learning. Geographically dispersed self-directed learners can learn in computer-supported communities, forming social learning networks. However, self-directed learners can suffer from a lack of continuous motivation. And surprisingly, social learning networks

Design of a Networked Learning Master Environment for Professionals

DEFF Research Database (Denmark)

Dirckinck-Holmfeld, Lone

2010-01-01

The paper is presenting the overall learning design of MIL (Master in ICT and Learning). The learning design is integrating a number of principles: 1. Principles of problem and project based learning 2. Networked learning / learning in communities of practice. The paper will discuss how these pri......The paper is presenting the overall learning design of MIL (Master in ICT and Learning). The learning design is integrating a number of principles: 1. Principles of problem and project based learning 2. Networked learning / learning in communities of practice. The paper will discuss how...

Teachers’ motives for learning in networks : costs, rewards and community interest

NARCIS (Netherlands)

van den Beemt, A.A.J.; Ketelaar, E.; Diepstraten, I.; de Laat, M.

2018-01-01

Background: This paper discusses teachers’ perspectives on learning networks and their motives for participating in these networks. Although it is widely held that teachers’ learning may be developed through learning networks, not all teachers participate in such networks. Purpose: The theme of

Teachers' Motives for Learning in Networks: Costs, Rewards and Community Interest

Science.gov (United States)

van den Beemt, Antoine; Ketelaar, Evelien; Diepstraten, Isabelle; de Laat, Maarten

2018-01-01

Background: This paper discusses teachers' perspectives on learning networks and their motives for participating in these networks. Although it is widely held that teachers' learning may be developed through learning networks, not all teachers participate in such networks. Purpose: The theme of reciprocity, central to studies in the area of…

Learning Networks, Networked Learning

NARCIS (Netherlands)

Sloep, Peter; Berlanga, Adriana

2010-01-01

Sloep, P. B., & Berlanga, A. J. (2011). Learning Networks, Networked Learning [Redes de Aprendizaje, Aprendizaje en Red]. Comunicar, XIX(37), 55-63. Retrieved from http://dx.doi.org/10.3916/C37-2011-02-05

Community Tracking in a cMooc and Nomadic Learner Behavior Identification on a Connectivist Rhizomatic Learning Network

Directory of Open Access Journals (Sweden)

Aras BOZKURT

2016-10-01

Full Text Available This article contributes to the literature on connectivism, connectivist MOOCs (cMOOCs and rhizomatic learning by examining participant interactions, community formation and nomadic learner behavior in a particular cMOOC, #rhizo15, facilitated for 6 weeks by Dave Cormier. It further focuses on what we can learn by observing Twitter interactions particularly. As an explanatory mixed research design, Social Network Analysis and content analysis were employed for the purposes of the research. SNA is used at the macro, meso and micro levels, and content analysis of one week of the MOOC was conducted using the Community of Inquiry framework. The macro level analysis demonstrates that communities in a rhizomatic connectivist networks have chaotic relationships with other communities in different dimensions (clarified by use of hashtags of concurrent, past and future events. A key finding at the meso level was that as #rhizo15 progressed and number of active participants decreased, interaction increased in overall network. The micro level analysis further reveals that, though completely online, the nature of open online ecosystems are very convenient to facilitate the formation of community. The content analysis of week 3 tweets demonstrated that cognitive presence was the most frequently observed, while teaching presence (teaching behaviors of both facilitator and participants was the lowest. This research recognizes the limitations of looking only at Twitter when #rhizo15 conversations occurred over multiple platforms frequented by overlapping but not identical groups of people. However, it provides a valuable partial perspective at the macro meso and micro levels that contribute to our understanding of community-building in cMOOCs.

Virtual Communities of Collaborative Learning for Higher Education

Directory of Open Access Journals (Sweden)

Gilda E. Sotomayor

2014-11-01

Full Text Available This article aims to outline and project three new learning scenarios for Higher Education that, after the emergence of ICT and communication through the Network-lnternet, have come under the generic name of virtual communities. To that end, we start from a previous conceptual analysis on collaborative learning, cooperative learning and related concepts taking place in these communities and serving as a basis for sorting them into three types in particular: communities of educational work of professional practice and scientific knowledge. Virtual communities where the activities undertaken and skills acquired are set as important parts of our personal learning development, wich are necessary to build the Knowledge Society.

Networks and learning: communities, practices and the metaphor of networks–a response

Directory of Open Access Journals (Sweden)

Chris Jones

2004-12-01

Full Text Available I am pleased to have the opportunity to react to Bruce Ingraham's response to my article ‘Networks and learning: communities, practices and the metaphor of networks' (Jones, 2004. It is rare to have a dialogue with someone who has taken the time and trouble to consider what you have written for a journal. All too often reviewing is a one-way process with the reviewer remaining anonymous. It is all the more pleasant to have a response to what you have written that gets to grips with some of the issues that the author also finds troubling. It is in that spirit that I write this reaction to Ingraham; it is an opportunity for me to develop some of the points he has identified as problematic in the original article. I want to concentrate on two main issues, firstly the network metaphor itself and secondly the usefulness of abstraction and representations of various types.

Enhancing Community Service Learning Via Practical Learning Communities

Directory of Open Access Journals (Sweden)

Ilana Ronen

2015-02-01

Full Text Available The advantages of learning communities focused on analyzing social issues and educational repercussions in the field are presented in this study. The research examines the contribution of a learning community to enhancing student teachers' responsibility and their social involvement. The assumption was that participating in learning community would further implement student teachers' community social involvement while enhancing responsibility in their field of action. A questionnaire aimed to present the student teachers' attitudes involving all aspects of studying in the learning community and their social activity in the community was conducted. The findings pinpointed that there were positive contributions of the learning communities from a personal aspect such as developing self-learning, and learning about “me”, as well as broaden their teaching skills, through methodology for teacher training, and developing reflective thought. These insights can also be implemented in various educational frameworks and during service learning as part of teacher training.

A model for evolution of overlapping community networks

Science.gov (United States)

Karan, Rituraj; Biswal, Bibhu

2017-05-01

A model is proposed for the evolution of network topology in social networks with overlapping community structure. Starting from an initial community structure that is defined in terms of group affiliations, the model postulates that the subsequent growth and loss of connections is similar to the Hebbian learning and unlearning in the brain and is governed by two dominant factors: the strength and frequency of interaction between the members, and the degree of overlap between different communities. The temporal evolution from an initial community structure to the current network topology can be described based on these two parameters. It is possible to quantify the growth occurred so far and predict the final stationary state to which the network is likely to evolve. Applications in epidemiology or the spread of email virus in a computer network as well as finding specific target nodes to control it are envisaged. While facing the challenge of collecting and analyzing large-scale time-resolved data on social groups and communities one faces the most basic questions: how do communities evolve in time? This work aims to address this issue by developing a mathematical model for the evolution of community networks and studying it through computer simulation.

Professional Learning in Unlikely Spaces: Social Media and Virtual Communities as Professional Development

Directory of Open Access Journals (Sweden)

Kathleen P. King

2011-12-01

Full Text Available In this case study, results demonstrate that an individual’s use of social media as professional learning spans understanding, networking, professional identity development, and transformative learning. Specifically, virtual online communities facilitated through social media provide professional networks, social relationships and learning beyond the scope of the individual’s usual experience. Case study method reveals strategies, extent, and impact of learning providing insight into this phenomenon. The significance of the research includes purposefully facilitating professional learning through informal learning contexts, including social media and online communities beyond technology-centric fields. Discussion and recommendations include using social media and virtual communities as instructional strategies for graduate studies and continued learning beyond formal education.

Building online learning communities in a graduate dental hygiene program.

Science.gov (United States)

Rogo, Ellen J; Portillo, Karen M

2014-08-01

The literature abounds with research related to building online communities in a single course; however, limited evidence is available on this phenomenon from a program perspective. The intent of this qualitative case study inquiry was to explore student experiences in a graduate dental hygiene program contributing or impeding the development and sustainability of online learning communities. Approval from the IRB was received. A purposive sampling technique was used to recruit participants from a stratification of students and graduates. A total of 17 participants completed semi-structured interviews. Data analysis was completed through 2 rounds - 1 for coding responses and 1 to construct categories of experiences. The participants' collective definition of an online learning community was a complex synergistic network of interconnected people who create positive energy. The findings indicated the development of this network began during the program orientation and was beneficial for building a foundation for the community. Students felt socially connected and supported by the network. Course design was another important category for participation in weekly discussions and group activities. Instructors were viewed as active participants in the community, offering helpful feedback and being a facilitator in discussions. Experiences impeding the development of online learning communities related to the poor performance of peers and instructors. Specific categories of experiences supported and impeded the development of online learning communities related to the program itself, course design, students and faculty. These factors are important to consider in order to maximize student learning potential in this environment. Copyright © 2014 The American Dental Hygienists’ Association.

Building research infrastructure in community health centers: a Community Health Applied Research Network (CHARN) report.

Science.gov (United States)

Likumahuwa, Sonja; Song, Hui; Singal, Robbie; Weir, Rosy Chang; Crane, Heidi; Muench, John; Sim, Shao-Chee; DeVoe, Jennifer E

2013-01-01

This article introduces the Community Health Applied Research Network (CHARN), a practice-based research network of community health centers (CHCs). Established by the Health Resources and Services Administration in 2010, CHARN is a network of 4 community research nodes, each with multiple affiliated CHCs and an academic center. The four nodes (18 individual CHCs and 4 academic partners in 9 states) are supported by a data coordinating center. Here we provide case studies detailing how CHARN is building research infrastructure and capacity in CHCs, with a particular focus on how community practice-academic partnerships were facilitated by the CHARN structure. The examples provided by the CHARN nodes include many of the building blocks of research capacity: communication capacity and "matchmaking" between providers and researchers; technology transfer; research methods tailored to community practice settings; and community institutional review board infrastructure to enable community oversight. We draw lessons learned from these case studies that we hope will serve as examples for other networks, with special relevance for community-based networks seeking to build research infrastructure in primary care settings.

Community-centred Networks and Networking among Companies, Educational and Cultural Institutions and Research

DEFF Research Database (Denmark)

Konnerup, Ulla; Dirckinck-Holmfeld, Lone

2010-01-01

This article presents visions for community-centred networks and networking among companies, educational and cultural institutions and research based on blended on- and off-line collaboration and communication. Our point of departure is the general vision of networking between government, industry...... and research as formulated in the Triple Helix Model (Etzkowitz 2008). The article draws on a case study of NoEL, a network on e-learning among business, educational and cultural institutions and research, all in all 21 partners from all around Denmark. Focus is how networks and networking change character......’ in Networked Learning, Wenger et al. 2009; The analysis concerns the participation structure and how the network activities connect local work practices and research, and how technology and online communication contribute to a change from participation in offline and physical network activities into online...

Virtual Communities of Collaborative Learning for Higher Education

Science.gov (United States)

Sotomayor, Gilda E.

2014-01-01

This article aims to outline and project three new learning scenarios for Higher Education that, after the emergence of ICT and communication through the Network-lnternet, have appeared under the generic name of virtual communities. To that end, we start from a previous conceptual analysis on collaborative learning, cooperative learning and…

Closer to Learning: Social Networks, Trust, and Professional Communities

Science.gov (United States)

Liou, Yi-Hwa; Daly, Alan J.

2014-01-01

Researchers, educators, and policymakers suggest the use of professional learning communities as one important approach to the improvement of teaching and learning. However, relatively little research examines the interplay of professional interactions (structural social capital) around instructional practices and key elements of professional…

Lessons Learned from the Young Breast Cancer Survivorship Network.

Science.gov (United States)

Gisiger-Camata, Silvia; Nolan, Timiya S; Vo, Jacqueline B; Bail, Jennifer R; Lewis, Kayla A; Meneses, Karen

2017-11-30

The Young Breast Cancer Survivors Network (Network) is an academic and community-based partnership dedicated to education, support, and networking. The Network used a multi-pronged approach via monthly support and networking, annual education seminars, website networking, and individual survivor consultation. Formative and summative evaluations were conducted using group survey and individual survivor interviews for monthly gatherings, annual education meetings, and individual consultation. Google Analytics was applied to evaluate website use. The Network began with 4 initial partnerships and grew to 38 in the period from 2011 to 2017. During this 5-year period, 5 annual meetings (598 attendees), 23 support and networking meetings (373), and 115 individual survivor consultations were conducted. The Network website had nearly 12,000 individual users and more than 25,000 page views. Lessons learned include active community engagement, survivor empowerment, capacity building, social media outreach, and network sustainability. The 5-year experiences with the Network demonstrated that a regional program dedicated to the education, support, networking, and needs of young breast cancer survivors and their families can become a vital part of cancer survivorship services in a community. Strong community support, engagement, and encouragement were vital components to sustain the program.

Social Networking Tools and Teacher Education Learning Communities: A Case Study

Science.gov (United States)

Poulin, Michael T.

2014-01-01

Social networking tools have become an integral part of a pre-service teacher's educational experience. As a result, the educational value of social networking tools in teacher preparation programs must be examined. The specific problem addressed in this study is that the role of social networking tools in teacher education learning communities…

Redes de aprendizaje, aprendizaje en red Learning Networks, Networked Learning

Directory of Open Access Journals (Sweden)

Peter Sloep

2011-10-01

Full Text Available Las redes de aprendizaje (Learning Networks son redes sociales en línea mediante las cuales los participantes comparten información y colaboran para crear conocimiento. De esta manera, estas redes enriquecen la experiencia de aprendizaje en cualquier contexto de aprendizaje, ya sea de educación formal (en escuelas o universidades o educación no-formal (formación profesional. Aunque el concepto de aprendizaje en red suscita el interés de diferentes actores del ámbito educativo, aún existen muchos interrogantes sobre cómo debe diseñarse el aprendizaje en red para facilitar adecuadamente la educación y la formación. El artículo toma este interrogante como punto de partida, y posteriormente aborda cuestiones como la dinámica de la evolución de las redes de aprendizaje, la importancia de fomentar la confianza entre los participantes y el papel central que desempeña el perfil de usuario en la construcción de la confianza, así como el apoyo entre compañeros. Además, se elabora el proceso de diseño de una red de aprendizaje, y se describe un ejemplo en el contexto universitario. Basándonos en la investigación que actualmente se lleva a cabo en nuestro propio centro y en otros lugares, el capítulo concluye con una visión del futuro de las redes de aprendizaje.Learning Networks are on-line social networks through which users share knowledge with each other and jointly develop new knowledge. This way, Learning Networks may enrich the experience of formal, school-based learning and form a viable setting for professional development. Although networked learning enjoys an increasing interest, many questions remain on how exactly learning in such networked contexts can contribute to successful education and training. Put differently, how should networked learning be designed best to facilitate education and training? Taking this as its point of departure, the chapter addresses such issues as the dynamic evolution of Learning Networks

The TENCompetence Infrastructure: A Learning Network Implementation

Science.gov (United States)

Vogten, Hubert; Martens, Harrie; Lemmers, Ruud

The TENCompetence project developed a first release of a Learning Network infrastructure to support individuals, groups and organisations in professional competence development. This infrastructure Learning Network infrastructure was released as open source to the community thereby allowing users and organisations to use and contribute to this development as they see fit. The infrastructure consists of client applications providing the user experience and server components that provide the services to these clients. These services implement the domain model (Koper 2006) by provisioning the entities of the domain model (see also Sect. 18.4) and henceforth will be referenced as domain entity services.

Personal Profiles: Enhancing Social Interaction in Learning Networks

NARCIS (Netherlands)

Berlanga, Adriana; Bitter-Rijpkema, Marlies; Brouns, Francis; Sloep, Peter; Fetter, Sibren

2009-01-01

Berlanga, A. J., Bitter-Rijpkema, M., Brouns, F., Sloep, P. B., & Fetter, S. (2011). Personal Profiles: Enhancing Social Interaction in Learning Networks. International Journal of Web Based Communities, 7(1), 66-82.

PARTNERS IN LEARNING NETWORK FOR UKRAINIAN TEACHERS

Directory of Open Access Journals (Sweden)

K. Sereda

2011-05-01

Full Text Available The network «Partners in Learning Network» is presented in the article – the Ukrainian segment of global educational community. PILN is created with support of the Microsoft company for teachers who use information communication technology in their professional work. The PILN's purpose and value for Ukrainian teachers, for their professional dialogue and collaboration are described in the article. Functions of PILN's communities for teacher’s cooperation, the joint decision of questions and an exchange of ideas and of technique, teaching tools for increase of level of ICT introduction in educational process are described.

Professional Learning in Unlikely Spaces: Social Media and Virtual Communities as Professional Development

OpenAIRE

Kathleen P. King

2011-01-01

In this case study, results demonstrate that an individual’s use of social media as professional learning spans understanding, networking, professional identity development, and transformative learning. Specifically, virtual online communities facilitated through social media provide professional networks, social relationships and learning beyond the scope of the individual’s usual experience. Case study method reveals strategies, extent, and impact of learning providing insight into this phe...

Rural Embedded Assistants for Community Health (REACH) network: first-person accounts in a community-university partnership.

Science.gov (United States)

Brown, Louis D; Alter, Theodore R; Brown, Leigh Gordon; Corbin, Marilyn A; Flaherty-Craig, Claire; McPhail, Lindsay G; Nevel, Pauline; Shoop, Kimbra; Sterner, Glenn; Terndrup, Thomas E; Weaver, M Ellen

2013-03-01

Community research and action projects undertaken by community-university partnerships can lead to contextually appropriate and sustainable community improvements in rural and urban localities. However, effective implementation is challenging and prone to failure when poorly executed. The current paper seeks to inform rural community-university partnership practice through consideration of first-person accounts from five stakeholders in the Rural Embedded Assistants for Community Health (REACH) Network. The REACH Network is a unique community-university partnership aimed at improving rural health services by identifying, implementing, and evaluating innovative health interventions delivered by local caregivers. The first-person accounts provide an insider's perspective on the nature of collaboration. The unique perspectives identify three critical challenges facing the REACH Network: trust, coordination, and sustainability. Through consideration of the challenges, we identified several strategies for success. We hope readers can learn their own lessons when considering the details of our partnership's efforts to improve the delivery infrastructure for rural healthcare.

Learning Analytics for Networked Learning Models

Science.gov (United States)

Joksimovic, Srecko; Hatala, Marek; Gaševic, Dragan

2014-01-01

Teaching and learning in networked settings has attracted significant attention recently. The central topic of networked learning research is human-human and human-information interactions occurring within a networked learning environment. The nature of these interactions is highly complex and usually requires a multi-dimensional approach to…

Networked Learning and Network Science: Potential Applications to Health Professionals' Continuing Education and Development.

Science.gov (United States)

Margolis, Alvaro; Parboosingh, John

2015-01-01

Prior interpersonal relationships and interactivity among members of professional associations may impact the learning process in continuing medical education (CME). On the other hand, CME programs that encourage interactivity between participants may impact structures and behaviors in these professional associations. With the advent of information and communication technologies, new communication spaces have emerged that have the potential to enhance networked learning in national and international professional associations and increase the effectiveness of CME for health professionals. In this article, network science, based on the application of network theory and other theories, is proposed as an approach to better understand the contribution networking and interactivity between health professionals in professional communities make to their learning and adoption of new practices over time. © 2015 The Alliance for Continuing Education in the Health Professions, the Society for Academic Continuing Medical Education, and the Council on Continuing Medical Education, Association for Hospital Medical Education.

Extending a configuration model to find communities in complex networks

International Nuclear Information System (INIS)

Jin, Di; Hu, Qinghua; He, Dongxiao; Yang, Bo; Baquero, Carlos

2013-01-01

Discovery of communities in complex networks is a fundamental data analysis task in various domains. Generative models are a promising class of techniques for identifying modular properties from networks, which has been actively discussed recently. However, most of them cannot preserve the degree sequence of networks, which will distort the community detection results. Rather than using a blockmodel as most current works do, here we generalize a configuration model, namely, a null model of modularity, to solve this problem. Towards decomposing and combining sub-graphs according to the soft community memberships, our model incorporates the ability to describe community structures, something the original model does not have. Also, it has the property, as with the original model, that it fixes the expected degree sequence to be the same as that of the observed network. We combine both the community property and degree sequence preserving into a single unified model, which gives better community results compared with other models. Thereafter, we learn the model using a technique of nonnegative matrix factorization and determine the number of communities by applying consensus clustering. We test this approach both on synthetic benchmarks and on real-world networks, and compare it with two similar methods. The experimental results demonstrate the superior performance of our method over competing methods in detecting both disjoint and overlapping communities. (paper)

STAR Library Education Network: a hands-on learning program for libraries and their communities

Science.gov (United States)

Dusenbery, P.

2010-12-01

Science and technology are widely recognized as major drivers of innovation and industry (e.g. Rising above the Gathering Storm, 2006). While the focus for education reform is on school improvement, there is considerable research that supports the role that out-of-school experiences can play in student achievement and public understanding of STEM disciplines. Libraries provide an untapped resource for engaging underserved youth and their families in fostering an appreciation and deeper understanding of science and technology topics. Designed spaces, like libraries, allow lifelong, life-wide, and life-deep learning to take place though the research basis for learning in libraries is not as developed as other informal settings like science centers. The Space Science Institute’s National Center for Interactive Learning (NCIL) in partnership with the American Library Association (ALA), the Lunar and Planetary Institute (LPI), and the National Girls Collaborative Project (NGCP) have received funding from NSF to develop a national education project called the STAR Library Education Network: a hands-on learning program for libraries and their communities (or STAR-Net for short). STAR stands for Science-Technology, Activities and Resources. The overarching goal of the project is to reach underserved youth and their families with informal STEM learning experiences. This project will deepen our knowledge of informal/lifelong learning that takes place in libraries and establish a learning model that can be compared to the more established free-choice learning model for science centers and museums. The project includes the development of two STEM hands-on exhibits on topics that are of interest to library staff and their patrons: Discover Earth and Discover Tech. In addition, the project will produce resources and inquiry-based activities that libraries can use to enrich the exhibit experience. Additional resources will be provided through partnerships with relevant

Learning Networks for Lifelong Learning

OpenAIRE

Sloep, Peter

2009-01-01

Presentation in a seminar organized by Christopher Hoadley at Penn State University, October 2004.Contains general introduction into the Learning Network Programme and a demonstration of the Netlogo Simulation of a Learning Network.

Using Web 2.0 for Learning in the Community

Science.gov (United States)

Mason, Robin; Rennie, Frank

2007-01-01

This paper describes the use of a range of Web 2.0 technologies to support the development of community for a newly formed Land Trust on the Isle of Lewis, in NW Scotland. The application of social networking tools in text, audio and video has several purposes: informal learning about the area to increase tourism, community interaction,…

Learning in networks Community of Practice: a new approach to entrepreneurial learning

NARCIS (Netherlands)

Schrooten, G.B.J. (Gerrit)

2009-01-01

Educational programs teaching entrepreneurial behaviour and knowledge are crucial to a vital and healthy economy. The concept of building a Communities of Practice (CoP) could be very promising. CoP’s are formed by people who engage in a process of collective learning in a shared domain of

On Deep Learning for Trust-Aware Recommendations in Social Networks.

Science.gov (United States)

Deng, Shuiguang; Huang, Longtao; Xu, Guandong; Wu, Xindong; Wu, Zhaohui

2017-05-01

With the emergence of online social networks, the social network-based recommendation approach is popularly used. The major benefit of this approach is the ability of dealing with the problems with cold-start users. In addition to social networks, user trust information also plays an important role to obtain reliable recommendations. Although matrix factorization (MF) becomes dominant in recommender systems, the recommendation largely relies on the initialization of the user and item latent feature vectors. Aiming at addressing these challenges, we develop a novel trust-based approach for recommendation in social networks. In particular, we attempt to leverage deep learning to determinate the initialization in MF for trust-aware social recommendations and to differentiate the community effect in user's trusted friendships. A two-phase recommendation process is proposed to utilize deep learning in initialization and to synthesize the users' interests and their trusted friends' interests together with the impact of community effect for recommendations. We perform extensive experiments on real-world social network data to demonstrate the accuracy and effectiveness of our proposed approach in comparison with other state-of-the-art methods.

A survey on social networks to determine requirements for Learning Networks for professional development of university staff

NARCIS (Netherlands)

Brouns, Francis; Berlanga, Adriana; Fetter, Sibren; Bitter-Rijpkema, Marlies; Van Bruggen, Jan; Sloep, Peter

2009-01-01

Brouns, F., Berlanga, A. J., Fetter, S., Bitter-Rijpkema, M. E., Van Bruggen, J. M., & Sloep, P. B. (2011). A survey on social networks to determine requirements for Learning Networks for professional development of university staff. International Journal of Web Based Communities, 7(3), 298-311.

CyberPsychological Computation on Social Community of Ubiquitous Learning

Science.gov (United States)

Zhou, Xuan; Dai, Genghui; Huang, Shuang; Sun, Xuemin; Hu, Feng; Hu, Hongzhi; Ivanović, Mirjana

2015-01-01

Under the modern network environment, ubiquitous learning has been a popular way for people to study knowledge, exchange ideas, and share skills in the cyberspace. Existing research findings indicate that the learners' initiative and community cohesion play vital roles in the social communities of ubiquitous learning, and therefore how to stimulate the learners' interest and participation willingness so as to improve their enjoyable experiences in the learning process should be the primary consideration on this issue. This paper aims to explore an effective method to monitor the learners' psychological reactions based on their behavioral features in cyberspace and therefore provide useful references for adjusting the strategies in the learning process. In doing so, this paper firstly analyzes the psychological assessment of the learners' situations as well as their typical behavioral patterns and then discusses the relationship between the learners' psychological reactions and their observable features in cyberspace. Finally, this paper puts forward a CyberPsychological computation method to estimate the learners' psychological states online. Considering the diversity of learners' habitual behaviors in the reactions to their psychological changes, a BP-GA neural network is proposed for the computation based on their personalized behavioral patterns. PMID:26557846

The overlapping community structure of structural brain network in young healthy individuals.

Directory of Open Access Journals (Sweden)

Kai Wu

2011-05-01

Full Text Available Community structure is a universal and significant feature of many complex networks in biology, society, and economics. Community structure has also been revealed in human brain structural and functional networks in previous studies. However, communities overlap and share many edges and nodes. Uncovering the overlapping community structure of complex networks remains largely unknown in human brain networks. Here, using regional gray matter volume, we investigated the structural brain network among 90 brain regions (according to a predefined anatomical atlas in 462 young, healthy individuals. Overlapped nodes between communities were defined by assuming that nodes (brain regions can belong to more than one community. We demonstrated that 90 brain regions were organized into 5 overlapping communities associated with several well-known brain systems, such as the auditory/language, visuospatial, emotion, decision-making, social, control of action, memory/learning, and visual systems. The overlapped nodes were mostly involved in an inferior-posterior pattern and were primarily related to auditory and visual perception. The overlapped nodes were mainly attributed to brain regions with higher node degrees and nodal efficiency and played a pivotal role in the flow of information through the structural brain network. Our results revealed fuzzy boundaries between communities by identifying overlapped nodes and provided new insights into the understanding of the relationship between the structure and function of the human brain. This study provides the first report of the overlapping community structure of the structural network of the human brain.

Learning Nursing in the Workplace Community: The Generation of Professional Capital

Science.gov (United States)

Gobbi, Mary

This chapter explores the connections between learning, working and professional communities in nursing. It draws on experiences and research in nursing practice and education, where not only do isolated professionals learn as a result of their actions for patients and others, but those professionals are part of a community whose associated networks enable learning to occur. Several characteristics of this professional community are shared with those found in Communities of Practice (CoPs) (Lave and Wenger, 1991; Wenger, 1998), but the balance and importance of many elements can differ. For instance, whilst Lave and Wenger (1991) describe many aspects of situated learning in CoPs that apply to nurses, their model is of little help in understanding the ways in which other professions as well as patients/clients and carers influence the development of nursing practice. Therefore, I shall argue that it is not just the Community of Practice that we need to consider

Influence of network communities and transition to web 3.0 on change of approaches

Directory of Open Access Journals (Sweden)

Ринат Гинаятович Рамазанов

2018-12-01

Full Text Available The article presents the genesis of network communities, the transformation of the interaction of participants in the evolution of this form of communication. The main characteristics and stages inherent in changing the formats of network interaction are described. The concept of networked communities is multifaceted and includes various processes of socialization: from the behaviour of people in interest groups to the formation of large international online communities. The evolution of such communities includes the transition from guest books and forums to more complex systems with many additional tools for networking. The introduction of modern technologies in all spheres of life leads to the formation of a qualitatively new distinctive model of education. In such conditions, the roles of participants in the educational process are changing, more attention is paid to self-education and development at the expense of the internal need for learning, the organizational and methodological component of the educational process is being reconstructed, and new conditions are created for the development of distance learning technologies. All this allows us to develop a more competitive system of interaction, make management processes transparent and create conditions for the independent development of each participant in the network space. Online network communities are a powerful tool that must be used in the education system in various interpretations, and as an additional resource that allows the principle of “lifelong learning” to be introduced into the teacher’s self-education system.

Community-Based Research Networks: Development and Lessons Learned in an Emerging Field.

Science.gov (United States)

Stoecker, Randy; Ambler, Susan H.; Cutforth, Nick; Donohue, Patrick; Dougherty, Dan; Marullo, Sam; Nelson, Kris S.; Stutts, Nancy B.

2003-01-01

Compares seven multi-institutional community-based research networks in Appalachia; Colorado; District of Columbia; Minneapolis-St. Paul; Philadelphia; Richmond, Virginia; and Trenton, New Jersey. After reviewing the histories of the networks, conducts a comparative SWOT analysis, showing their common and unique strengths, weaknesses,…

Community detection using preference networks

Science.gov (United States)

Tasgin, Mursel; Bingol, Haluk O.

2018-04-01

Community detection is the task of identifying clusters or groups of nodes in a network where nodes within the same group are more connected with each other than with nodes in different groups. It has practical uses in identifying similar functions or roles of nodes in many biological, social and computer networks. With the availability of very large networks in recent years, performance and scalability of community detection algorithms become crucial, i.e. if time complexity of an algorithm is high, it cannot run on large networks. In this paper, we propose a new community detection algorithm, which has a local approach and is able to run on large networks. It has a simple and effective method; given a network, algorithm constructs a preference network of nodes where each node has a single outgoing edge showing its preferred node to be in the same community with. In such a preference network, each connected component is a community. Selection of the preferred node is performed using similarity based metrics of nodes. We use two alternatives for this purpose which can be calculated in 1-neighborhood of nodes, i.e. number of common neighbors of selector node and its neighbors and, the spread capability of neighbors around the selector node which is calculated by the gossip algorithm of Lind et.al. Our algorithm is tested on both computer generated LFR networks and real-life networks with ground-truth community structure. It can identify communities accurately in a fast way. It is local, scalable and suitable for distributed execution on large networks.

Research, Boundaries, and Policy in Networked Learning

DEFF Research Database (Denmark)

This book presents cutting-edge, peer reviewed research on networked learning organized by three themes: policy in networked learning, researching networked learning, and boundaries in networked learning. The "policy in networked learning" section explores networked learning in relation to policy...... networks, spaces of algorithmic governance and more. The "boundaries in networked learning" section investigates frameworks of students' digital literacy practices, among other important frameworks in digital learning. Lastly, the "research in networked learning" section delves into new research methods...

Magnets and Seekers: A Network Perspective on Academic Integration inside Two Residential Communities

Science.gov (United States)

Smith, Rachel A.

2015-01-01

Residential learning communities aim to foster increased academic and social integration, ideally leading to greater student success. However, the concept of academic integration is often conceptualized and measured at the individual level, rather than the theoretically more consistent community level. Network analysis provides a paradigm and…

Conditions for Productive Learning in Network Learning Environments

DEFF Research Database (Denmark)

Ponti, M.; Dirckinck-Holmfeld, Lone; Lindström, B.

2004-01-01

are designed without a deep understanding of the pedagogical, communicative and collaborative conditions embedded in networked learning. Despite the existence of good theoretical views pointing to a social understanding of learning, rather than a traditional individualistic and information processing approach......The Kaleidoscope1 Jointly Executed Integrating Research Project (JEIRP) on Conditions for Productive Networked Learning Environments is developing and elaborating conceptual understandings of Computer Supported Collaborative Learning (CSCL) emphasizing the use of cross-cultural comparative......: Pedagogical design and the dialectics of the digital artefacts, the concept of collaboration, ethics/trust, identity and the role of scaffolding of networked learning environments.   The JEIRP is motivated by the fact that many networked learning environments in various European educational settings...

Social Media as Avenue for Personal Learning for Educators: Personal Learning Networks Encourage Application of Knowledge and Skills

Science.gov (United States)

Eller, Linda S.

2012-01-01

Social media sites furnish an online space for a community of practice to create relationships and trust, collaboration and connections, and a personal learning environment. Social networking sites, both public and private, have common elements: member profiles, groups, discussions, and forums. A community of practice brings participants together…

Learning Networks for Professional Development & Lifelong Learning

NARCIS (Netherlands)

Brouns, Francis; Sloep, Peter

2009-01-01

Brouns, F., & Sloep, P. B. (2009). Learning Networks for Professional Development & Lifelong Learning. Presentation of the Learning Network Programme for a Korean delegation of Chonnam National University and Dankook University (researchers dr. Jeeheon Ryu and dr. Minjeong Kim and a Group of PhD and

THE INTEGRATION OF CREATIVITY MANAGEMENT MODELS INTO UNIVERSITIES’VIRTUAL LEARNING COMMUNITIES

Directory of Open Access Journals (Sweden)

Alexandru STRUNGĂ

2014-12-01

Full Text Available Given the access of an increasingly higher number of individuals to virtual learning networks, the issue of creativity management becomes extremely important, especially for schools and universities. In the specialized literature, participating in virtual learning communities has several advantages, including: permanent access to information, high educational performance and increased creativity, and also better-developed professional identity (North and Kumta, 2014; Boulay and van Raalte, 2014. In the Romanian literature, there are few studies that aim directly at the relationship between the participation in virtual learning networks and creativity and innovation management models, especially in higher education institutions. This paper aims to study the ways in which creativity and innovation management models can be used in virtual learning networks in order to achieve better productivity at both individual and organizational levels, taking into account several best practices from this field and their possible implementation in Romanian educational institutions.

A Community Network of 100 Black Carbon Sensors

Science.gov (United States)

Preble, C.; Kirchstetter, T.; Caubel, J.; Cados, T.; Keeling, C.; Chang, S.

2017-12-01

We developed a low-cost black carbon sensor, field tested its performance, and then built and deployed a network of 100 sensors in West Oakland, California. We operated the network for 100 days beginning mid-May 2017 to measure spatially resolved black carbon concentrations throughout the community. West Oakland is a San Francisco Bay Area mixed residential and industrial community that is adjacent to regional port and rail yard facilities and surrounded by major freeways. As such, the community is affected by diesel particulate matter emissions from heavy-duty diesel trucks, locomotives, and ships associated with freight movement. In partnership with Environmental Defense Fund, the Bay Area Air Quality Management District, and the West Oakland Environmental Indicators Project, we deployed the black carbon monitoring network outside of residences and business, along truck routes and arterial streets, and at upwind locations. The sensor employs the filter-based light transmission method to measure black carbon and has good precision and correspondence with current commercial black carbon instruments. Throughout the 100-day period, each of the 100 sensors transmitted data via a cellular network. A MySQL database was built to receive and manage the data in real-time. The database included diagnostic features to monitor each sensor's operational status and facilitate the maintenance of the network. Spatial and temporal patterns in black carbon concentrations will be presented, including patterns around industrial facilities, freeways, and truck routes, as well as the relationship between neighborhood concentrations and the BAAQMD's monitoring site. Lessons learned during this first of its kind black carbon monitoring network will also be shared.

Disseminating Innovations in Teaching Value-Based Care Through an Online Learning Network.

Science.gov (United States)

Gupta, Reshma; Shah, Neel T; Moriates, Christopher; Wallingford, September; Arora, Vineet M

2017-08-01

A national imperative to provide value-based care requires new strategies to teach clinicians about high-value care. We developed a virtual online learning network aimed at disseminating emerging strategies in teaching value-based care. The online Teaching Value in Health Care Learning Network includes monthly webinars that feature selected innovators, online discussion forums, and a repository for sharing tools. The learning network comprises clinician-educators and health system leaders across North America. We conducted a cross-sectional online survey of all webinar presenters and the active members of the network, and we assessed program feasibility. Six months after the program launched, there were 277 learning community members in 22 US states. Of the 74 active members, 50 (68%) completed the evaluation. Active members represented independently practicing physicians and trainees in 7 specialties, nurses, educators, and health system leaders. Nearly all speakers reported that the learning network provided them with a unique opportunity to connect with a different audience and achieve greater recognition for their work. Of the members who were active in the learning network, most reported that strategies gleaned from the network were helpful, and some adopted or adapted these innovations at their home institutions. One year after the program launched, the learning network had grown to 364 total members. The learning network helped participants share and implement innovations to promote high-value care. The model can help disseminate innovations in emerging areas of health care transformation, and is sustainable without ongoing support after a period of start-up funding.

Network Learning and Innovation in SME Formal Networks

Directory of Open Access Journals (Sweden)

Jivka Deiters

2013-02-01

Full Text Available The driver for this paper is the need to better understand the potential for learning and innovation that networks canprovide especially for small and medium sized enterprises (SMEs which comprise by far the majority of enterprises in the food sector. With the challenges the food sector is facing in the near future, learning and innovation or more focused, as it is being discussed in the paper, ‘learning for innovation’ are not just opportunities but pre‐conditions for the sustainability of the sector. Network initiatives that could provide appropriate support involve social interaction and knowledge exchange, learning, competence development, and coordination (organization and management of implementation. The analysis identifies case studies in any of these orientations which serve different stages of the innovation process: invention and implementation. The variety of network case studies cover networks linked to a focus group for training, research, orconsulting, networks dealing with focused market oriented product or process development, promotional networks, and networks for open exchange and social networking.

RELATION BETWEEN COOPERATION AND ORGANIZATIONAL LEARNING WITH THE COMPETITIVENESS IN AN INTERORGANIZATIONAL NETWORK

Directory of Open Access Journals (Sweden)

Paulo Cesar Zonta

2015-05-01

Full Text Available The study analyzed the relationship between cooperation and organizational learning with competitiveness in a small and medium enterprises (SME network, with business of the groups of the Commercial and Industrial Association of Chapecó (ACIC. The methodology used was quantitative, with the factorial analysis. Currently, ACIC has 14 groups and 236 SME´s nucleated, developing joint activities of economic and social sustainability in Chapecó. The theoretical study raised concepts already endorsed by the scientific community on interorganizational networks, competitiveness, cooperation and organizational learning. The results demonstrated that indicators related to cooperation and learning in horizontal networks are characterized as antecedents of competitiveness in organizational networks, and that there is a positive correlation between the constructs cooperation and organizational learning with competitiveness construct. The study confirms the belief that small businesses associated in networks can increase their competitiveness, thus contributing to regional development.

Community, Collective or Movement? Evaluating Theoretical Perspectives on Network Building

Science.gov (United States)

Spitzer, W.

2015-12-01

Since 2007, the New England Aquarium has led a national effort to increase the capacity of informal science venues to effectively communicate about climate change. We are now leading the NSF-funded National Network for Ocean and Climate Change Interpretation (NNOCCI), partnering with the Association of Zoos and Aquariums, FrameWorks Institute, Woods Hole Oceanographic Institution, Monterey Bay Aquarium, and National Aquarium, with evaluation conducted by the New Knowledge Organization, Pennsylvania State University, and Ohio State University. NNOCCI enables teams of informal science interpreters across the country to serve as "communication strategists" - beyond merely conveying information they can influence public perceptions, given their high level of commitment, knowledge, public trust, social networks, and visitor contact. We provide in-depth training as well as an alumni network for ongoing learning, implementation support, leadership development, and coalition building. Our goals are to achieve a systemic national impact, embed our work within multiple ongoing regional and national climate change education networks, and leave an enduring legacy. What is the most useful theoretical model for conceptualizing the work of the NNOCCI community? This presentation will examine the pros and cons of three perspectives -- community of practice, collective impact, and social movements. The community of practice approach emphasizes use of common tools, support for practice, social learning, and organic development of leadership. A collective impact model focuses on defining common outcomes, aligning activities toward a common goal, structured collaboration. A social movement emphasizes building group identity and creating a sense of group efficacy. This presentation will address how these models compare in terms of their utility in program planning and evaluation, their fit with the unique characteristics of the NNOCCI community, and their relevance to our program goals.

Searching for Communities in Bipartite Networks

OpenAIRE

Barber, Michael J.; Faria, Margarida; Streit, Ludwig; Strogan, Oleg

2008-01-01

Bipartite networks are a useful tool for representing and investigating interaction networks. We consider methods for identifying communities in bipartite networks. Intuitive notions of network community groups are made explicit using Newman's modularity measure. A specialized version of the modularity, adapted to be appropriate for bipartite networks, is presented; a corresponding algorithm is described for identifying community groups through maximizing this measure. The algorithm is applie...

Network-Based Community Brings forth Sustainable Society

Science.gov (United States)

Kikuchi, Toshiko

It has already been shown that an artificial society based on the three relations of social configuration (market, communal, and obligatory relations) functioning in balance with each other formed a sustainable society which the social reproduction is possible. In this artificial society model, communal relations exist in a network-based community with alternating members rather than a conventional community with cooperative mutual assistance practiced in some agricultural communities. In this paper, using the comparison between network-based communities with alternating members and conventional communities with fixed members, the significance of a network-based community is considered. In concrete terms, the difference in appearance rate for sustainable society, economic activity and asset inequality between network-based communities and conventional communities is analyzed. The appearance rate for a sustainable society of network-based community is higher than that of conventional community. Moreover, most of network-based communities had a larger total number of trade volume than conventional communities. But, the value of Gini coefficient in conventional community is smaller than that of network-based community. These results show that communal relations based on a network-based community is significant for the social reproduction and economic efficiency. However, in such an artificial society, the inequality is sacrificed.

Building mathematics cellular phone learning communities

Directory of Open Access Journals (Sweden)

Wajeeh M. Daher

2011-04-01

Full Text Available Researchers emphasize the importance of maintaining learning communities and environments. This article describes the building and nourishment of a learning community, one comprised of middle school students who learned mathematics out-of-class using the cellular phone. The building of the learning community was led by three third year pre-service teachers majoring in mathematics and computers. The pre-service teachers selected thirty 8th grade students to learn mathematics with the cellular phone and be part of a learning community experimenting with this learning. To analyze the building and development stages of the cellular phone learning community, two models of community building stages were used; first the team development model developed by Tuckman (1965, second the life cycle model of a virtual learning community developed by Garber (2004. The research findings indicate that a learning community which is centered on a new technology has five 'life' phases of development: Pre-birth, birth, formation, performing, and maturity. Further, the research finding indicate that the norms that were encouraged by the preservice teachers who initiated the cellular phone learning community resulted in a community which developed, nourished and matured to be similar to a community of experienced applied mathematicians who use mathematical formulae to study everyday phenomena.

Collaborative Learning in the Scientific Community of Practice

International Nuclear Information System (INIS)

Jesionkowska, J.

2016-01-01

Full text: The paper describes research done in the scope of doctoral project. The aim of the study is to discover how to improve the process of collaborative learning in the community of scientists by the development of a community of practice. A mixed methods approach was used combining data from content analysis, interviews and questionnaires. Results show that such community helps to build relationships and network with others, it motivates to share work-related knowledge, represents an area of common interest for organization, but also that it is mainly driven by the willingness of members and is lacking instruments to share ideas. (author

An evolving network model with community structure

International Nuclear Information System (INIS)

Li Chunguang; Maini, Philip K

2005-01-01

Many social and biological networks consist of communities-groups of nodes within which connections are dense, but between which connections are sparser. Recently, there has been considerable interest in designing algorithms for detecting community structures in real-world complex networks. In this paper, we propose an evolving network model which exhibits community structure. The network model is based on the inner-community preferential attachment and inter-community preferential attachment mechanisms. The degree distributions of this network model are analysed based on a mean-field method. Theoretical results and numerical simulations indicate that this network model has community structure and scale-free properties

Red de comunidades de aprendizaje, un espacio para la formación de formadores/ Learning network community, a space for the formation of teachers

Directory of Open Access Journals (Sweden)

Jorge Iván Ríos Rivera

2007-01-01

Full Text Available Cuando se habla de ambientes de aprendizaje se acude a un concepto muy necesitado para el desarrollo de la educación de hoy. Este, no se ha abordado, de manera detenida y profunda cuando se incorpora en los discursos de las TIC. Por ello se presenta en una de las materializaciones más promisorias del momento, llamada las comunidades de aprendizaje, además, pueden ser pensadas como espacios de formación de formadores dónde se propician muchas de las transformaciones requeridas en los docentes para la educación del nuevo mileno. El texto parte de la idea de formar a un maestro para contrarrestar el impacto de la escuela paralela a través de la creación de un “ecosistema comunicativo”, se relaciona la noción de ambiente de aprendizaje, en cada uno de sus componentes, Ambiente y aprendizaje, con el concepto de Redes de aprendizaje y comunidades de aprendizaje, para finalmente, ilustrar el modelo de red de comunidades de aprendizaje vivido desde la experiencia del proyecto REDES (MEN-UPB. When it is talked about learning environments, it is referred a much needed concept to the development of the nowadays education. This concept has not been treated in a deep and consciously way, when it has been incorporated to the speeches about the Information and communication technology (ICT. That is why is presented in one of the most hopefully materialization: the learning community, this could be also thought as a formation space for educators, a space where are favored many of the required transformation of the new millennium teachers. This article begins with the idea of educate a teacher, to counter the parallel school impact through the creation of a “communicative ecosystem”, then it is related the notion of learning environment, in each one of its components: environment and learning, with the concept of learning network and learning community. This article finishes with the illustration of the learning community network model

What's the VALUE of Information Literacy? Comparing Learning Community and Non-Learning Community Student Learning Outcomes

Science.gov (United States)

Rapchak, Marcia E.; Brungard, Allison B.; Bergfelt, Theodore W.

2016-01-01

Using the Information Literacy VALUE Rubric provided by the AAC&U, this study compares thirty final capstone assignments in a research course in a learning community with thirty final assignments in from students not in learning communities. Results indicated higher performance of the non-learning community students; however, transfer skills…

Finding overlapping communities in multilayer networks.

Science.gov (United States)

Liu, Weiyi; Suzumura, Toyotaro; Ji, Hongyu; Hu, Guangmin

2018-01-01

Finding communities in multilayer networks is a vital step in understanding the structure and dynamics of these layers, where each layer represents a particular type of relationship between nodes in the natural world. However, most community discovery methods for multilayer networks may ignore the interplay between layers or the unique topological structure in a layer. Moreover, most of them can only detect non-overlapping communities. In this paper, we propose a new community discovery method for multilayer networks, which leverages the interplay between layers and the unique topology in a layer to reveal overlapping communities. Through a comprehensive analysis of edge behaviors within and across layers, we first calculate the similarities for edges from the same layer and the cross layers. Then, by leveraging these similarities, we can construct a dendrogram for the multilayer networks that takes both the unique topological structure and the important interplay into consideration. Finally, by introducing a new community density metric for multilayer networks, we can cut the dendrogram to get the overlapping communities for these layers. By applying our method on both synthetic and real-world datasets, we demonstrate that our method has an accurate performance in discovering overlapping communities in multilayer networks.

Local communities obstruct global consensus: Naming game on multi-local-world networks

Science.gov (United States)

Lou, Yang; Chen, Guanrong; Fan, Zhengping; Xiang, Luna

2018-02-01

Community structure is essential for social communications, where individuals belonging to the same community are much more actively interacting and communicating with each other than those in different communities within the human society. Naming game, on the other hand, is a social communication model that simulates the process of learning a name of an object within a community of humans, where the individuals can generally reach global consensus asymptotically through iterative pair-wise conversations. The underlying network indicates the relationships among the individuals. In this paper, three typical topologies, namely random-graph, small-world and scale-free networks, are employed, which are embedded with the multi-local-world community structure, to study the naming game. Simulations show that (1) the convergence process to global consensus is getting slower as the community structure becomes more prominent, and eventually might fail; (2) if the inter-community connections are sufficiently dense, neither the number nor the size of the communities affects the convergence process; and (3) for different topologies with the same (or similar) average node-degree, local clustering of individuals obstruct or prohibit global consensus to take place. The results reveal the role of local communities in a global naming game in social network studies.

Material Matters for Learning in Virtual Networks: A Case Study of a Professional Learning Programme Hosted in a Google+ Online Community

Science.gov (United States)

Ackland, Aileen; Swinney, Ann

2015-01-01

In this paper, we draw on Actor-Network Theories (ANT) to explore how material components functioned to create gateways and barriers to a virtual learning network in the context of a professional development module in higher education. Students were practitioners engaged in family learning in different professional roles and contexts. The data…

Using Social Network Analysis to Evaluate Community Capacity Building of a Regional Community Cancer Network

Science.gov (United States)

Luque, John; Tyson, Dinorah Martinez; Lee, Ji-Hyun; Gwede, Clement; Vadaparampil, Susan; Noel-Thomas, Shalewa; Meade, Cathy

2010-01-01

The Tampa Bay Community Cancer Network (TBCCN) is one of 25 Community Network Programs funded by the National Cancer Institute's (NCI's) Center to Reduce Cancer Health Disparities with the objectives to create a collaborative infrastructure of academic and community based organizations and to develop effective and sustainable interventions to…

Robust Learning of High-dimensional Biological Networks with Bayesian Networks

Science.gov (United States)

Nägele, Andreas; Dejori, Mathäus; Stetter, Martin

Structure learning of Bayesian networks applied to gene expression data has become a potentially useful method to estimate interactions between genes. However, the NP-hardness of Bayesian network structure learning renders the reconstruction of the full genetic network with thousands of genes unfeasible. Consequently, the maximal network size is usually restricted dramatically to a small set of genes (corresponding with variables in the Bayesian network). Although this feature reduction step makes structure learning computationally tractable, on the downside, the learned structure might be adversely affected due to the introduction of missing genes. Additionally, gene expression data are usually very sparse with respect to the number of samples, i.e., the number of genes is much greater than the number of different observations. Given these problems, learning robust network features from microarray data is a challenging task. This chapter presents several approaches tackling the robustness issue in order to obtain a more reliable estimation of learned network features.

Community Based Learning and Civic Engagement: Informal Learning among Adult Volunteers in Community Organizations

Science.gov (United States)

Mundel, Karsten; Schugurensky, Daniel

2008-01-01

Many iterations of community based learning employ models, such as consciousness raising groups, cultural circles, and participatory action research. In all of them, learning is a deliberate part of an explicit educational activity. This article explores another realm of community learning: the informal learning that results from volunteering in…

Usefulness of an Internet-based thematic learning network: comparison of effectiveness with traditional teaching.

Science.gov (United States)

Coma Del Corral, María Jesús; Guevara, José Cordero; Luquin, Pedro Abáigar; Peña, Horacio J; Mateos Otero, Juan José

2006-03-01

UniNet is an Internet-based thematic network for a virtual community of users (VCU). It supports one multidisciplinary community of doctoral students, who receive most of the courses on the network. The evident advantages of distance learning by Internet, in terms of costs, comfort, etc., require a previous evaluation of the system, focusing on the learning outcomes of the student. The aim was to evaluate the real learning of the students of doctorate courses, by comparing the effectiveness of distance learning in UniNet with traditional classroom-based teaching. Five doctorate courses were taught simultaneously to two independent groups of students in two ways: one, through the UniNet Network, and the other in a traditional classroom. The academic knowledge of students was evaluated at the beginning and end of each course. The difference in score was considered as a knowledge increase. The comparison was made using Student's t-test for independent groups. There were no significant statistical differences in the outcomes of the two groups of students. This suggests that both teaching systems were equivalent in increasing the knowledge of the students. Both educational methods, the traditional system and the online system in a thematic network, are effective and similar for increasing knowledge.

Neural networks and statistical learning

CERN Document Server

Du, Ke-Lin

2014-01-01

Providing a broad but in-depth introduction to neural network and machine learning in a statistical framework, this book provides a single, comprehensive resource for study and further research. All the major popular neural network models and statistical learning approaches are covered with examples and exercises in every chapter to develop a practical working understanding of the content. Each of the twenty-five chapters includes state-of-the-art descriptions and important research results on the respective topics. The broad coverage includes the multilayer perceptron, the Hopfield network, associative memory models, clustering models and algorithms, the radial basis function network, recurrent neural networks, principal component analysis, nonnegative matrix factorization, independent component analysis, discriminant analysis, support vector machines, kernel methods, reinforcement learning, probabilistic and Bayesian networks, data fusion and ensemble learning, fuzzy sets and logic, neurofuzzy models, hardw...

Blending Formal and Informal Learning Networks for Online Learning

Science.gov (United States)

Czerkawski, Betül C.

2016-01-01

With the emergence of social software and the advance of web-based technologies, online learning networks provide invaluable opportunities for learning, whether formal or informal. Unlike top-down, instructor-centered, and carefully planned formal learning settings, informal learning networks offer more bottom-up, student-centered participatory…

Networked professional learning

NARCIS (Netherlands)

Sloep, Peter

2013-01-01

Sloep, P. B. (2013). Networked professional learning. In A. Littlejohn, & A. Margaryan (Eds.), Technology-enhanced Professional Learning: Processes, Practices and Tools (pp. 97–108). London: Routledge.

Creative Network Communities in the Translocal Space of Digital Networks

Directory of Open Access Journals (Sweden)

Rasa Smite

2013-01-01

Full Text Available What should sociological research be in the age of Web 2.0? Considering that the task of “network sociology” is not only empirical research but also the interpretation of tendencies of the network culture, this research explores the rise of network communities within Eastern and Western Europe in the early Internet era. I coined the term creative networks to distinguish these early creative and social activities from today’s popular social networking. Thus I aimed to interpret the meaning of social action; the motivation of creative community actors, their main fields of activities and social organization forms; and the potential that these early developments contain for the future sustainability of networks. Data comprise interviews with networking experts and founders and members of various networks. Investigating respondents’ motivations for creating online networks and communities, and interpreting those terms, allows for comparing the creative networks of the 1990s with today’s social networks and for drawing conclusions.

Building Ocean Learning Communities: A COSEE Science and Education Partnership

Science.gov (United States)

Robigou, V.; Bullerdick, S.; Anderson, A.

2007-12-01

The core mission of the Centers for Ocean Sciences Education Excellence (COSEE) is to promote partnerships between research scientists and educators through a national network of regional and thematic centers. In addition, the COSEEs also disseminate best practices in ocean sciences education, and promote ocean sciences as a charismatic interdisciplinary vehicle for creating a more scientifically literate workforce and citizenry. Although each center is mainly funded through a peer-reviewed grant process by the National Science Foundation (NSF), the centers form a national network that fosters collaborative efforts among the centers to design and implement initiatives for the benefit of the entire network and beyond. Among these initiatives the COSEE network has contributed to the definition, promotion, and dissemination of Ocean Literacy in formal and informal learning settings. Relevant to all research scientists, an Education and Public Outreach guide for scientists is now available at www.tos.org. This guide highlights strategies for engaging scientists in Ocean Sciences Education that are often applicable in other sciences. To address the challenging issue of ocean sciences education informed by scientific research, the COSEE approach supports centers that are partnerships between research institutions, formal and informal education venues, advocacy groups, industry, and others. The COSEE Ocean Learning Communities, is a partnership between the University of Washington College of Ocean and Fishery Sciences and College of Education, the Seattle Aquarium, and a not-for-profit educational organization. The main focus of the center is to foster and create Learning Communities that cultivate contributing, and ocean sciences-literate citizens aware of the ocean's impact on daily life. The center is currently working with volunteer groups around the Northwest region that are actively involved in projects in the marine environment and to empower these diverse groups

Epidemic spreading on complex networks with community structures

NARCIS (Netherlands)

Stegehuis, C.; van der Hofstad, R.W.; van Leeuwaarden, J.S.H.

2016-01-01

Many real-world networks display a community structure. We study two random graph models that create a network with similar community structure as a given network. One model preserves the exact community structure of the original network, while the other model only preserves the set of communities

Multilabel user classification using the community structure of online networks.

Science.gov (United States)

Rizos, Georgios; Papadopoulos, Symeon; Kompatsiaris, Yiannis

2017-01-01

We study the problem of semi-supervised, multi-label user classification of networked data in the online social platform setting. We propose a framework that combines unsupervised community extraction and supervised, community-based feature weighting before training a classifier. We introduce Approximate Regularized Commute-Time Embedding (ARCTE), an algorithm that projects the users of a social graph onto a latent space, but instead of packing the global structure into a matrix of predefined rank, as many spectral and neural representation learning methods do, it extracts local communities for all users in the graph in order to learn a sparse embedding. To this end, we employ an improvement of personalized PageRank algorithms for searching locally in each user's graph structure. Then, we perform supervised community feature weighting in order to boost the importance of highly predictive communities. We assess our method performance on the problem of user classification by performing an extensive comparative study among various recent methods based on graph embeddings. The comparison shows that ARCTE significantly outperforms the competition in almost all cases, achieving up to 35% relative improvement compared to the second best competing method in terms of F1-score.

Multilabel user classification using the community structure of online networks.

Directory of Open Access Journals (Sweden)

Georgios Rizos

Full Text Available We study the problem of semi-supervised, multi-label user classification of networked data in the online social platform setting. We propose a framework that combines unsupervised community extraction and supervised, community-based feature weighting before training a classifier. We introduce Approximate Regularized Commute-Time Embedding (ARCTE, an algorithm that projects the users of a social graph onto a latent space, but instead of packing the global structure into a matrix of predefined rank, as many spectral and neural representation learning methods do, it extracts local communities for all users in the graph in order to learn a sparse embedding. To this end, we employ an improvement of personalized PageRank algorithms for searching locally in each user's graph structure. Then, we perform supervised community feature weighting in order to boost the importance of highly predictive communities. We assess our method performance on the problem of user classification by performing an extensive comparative study among various recent methods based on graph embeddings. The comparison shows that ARCTE significantly outperforms the competition in almost all cases, achieving up to 35% relative improvement compared to the second best competing method in terms of F1-score.

Learning conditional Gaussian networks

DEFF Research Database (Denmark)

Bøttcher, Susanne Gammelgaard

This paper considers conditional Gaussian networks. The parameters in the network are learned by using conjugate Bayesian analysis. As conjugate local priors, we apply the Dirichlet distribution for discrete variables and the Gaussian-inverse gamma distribution for continuous variables, given...... a configuration of the discrete parents. We assume parameter independence and complete data. Further, to learn the structure of the network, the network score is deduced. We then develop a local master prior procedure, for deriving parameter priors in these networks. This procedure satisfies parameter...... independence, parameter modularity and likelihood equivalence. Bayes factors to be used in model search are introduced. Finally the methods derived are illustrated by a simple example....

A divisive spectral method for network community detection

International Nuclear Information System (INIS)

Cheng, Jianjun; Li, Longjie; Yao, Yukai; Chen, Xiaoyun; Leng, Mingwei; Lu, Weiguo

2016-01-01

Community detection is a fundamental problem in the domain of complex network analysis. It has received great attention, and many community detection methods have been proposed in the last decade. In this paper, we propose a divisive spectral method for identifying community structures from networks which utilizes a sparsification operation to pre-process the networks first, and then uses a repeated bisection spectral algorithm to partition the networks into communities. The sparsification operation makes the community boundaries clearer and sharper, so that the repeated spectral bisection algorithm extract high-quality community structures accurately from the sparsified networks. Experiments show that the combination of network sparsification and a spectral bisection algorithm is highly successful, the proposed method is more effective in detecting community structures from networks than the others. (paper: interdisciplinary statistical mechanics)

Decoding communities in networks.

Science.gov (United States)

Radicchi, Filippo

2018-02-01

According to a recent information-theoretical proposal, the problem of defining and identifying communities in networks can be interpreted as a classical communication task over a noisy channel: memberships of nodes are information bits erased by the channel, edges and nonedges in the network are parity bits introduced by the encoder but degraded through the channel, and a community identification algorithm is a decoder. The interpretation is perfectly equivalent to the one at the basis of well-known statistical inference algorithms for community detection. The only difference in the interpretation is that a noisy channel replaces a stochastic network model. However, the different perspective gives the opportunity to take advantage of the rich set of tools of coding theory to generate novel insights on the problem of community detection. In this paper, we illustrate two main applications of standard coding-theoretical methods to community detection. First, we leverage a state-of-the-art decoding technique to generate a family of quasioptimal community detection algorithms. Second and more important, we show that the Shannon's noisy-channel coding theorem can be invoked to establish a lower bound, here named as decodability bound, for the maximum amount of noise tolerable by an ideal decoder to achieve perfect detection of communities. When computed for well-established synthetic benchmarks, the decodability bound explains accurately the performance achieved by the best community detection algorithms existing on the market, telling us that only little room for their improvement is still potentially left.

Decoding communities in networks

Science.gov (United States)

Radicchi, Filippo

2018-02-01

According to a recent information-theoretical proposal, the problem of defining and identifying communities in networks can be interpreted as a classical communication task over a noisy channel: memberships of nodes are information bits erased by the channel, edges and nonedges in the network are parity bits introduced by the encoder but degraded through the channel, and a community identification algorithm is a decoder. The interpretation is perfectly equivalent to the one at the basis of well-known statistical inference algorithms for community detection. The only difference in the interpretation is that a noisy channel replaces a stochastic network model. However, the different perspective gives the opportunity to take advantage of the rich set of tools of coding theory to generate novel insights on the problem of community detection. In this paper, we illustrate two main applications of standard coding-theoretical methods to community detection. First, we leverage a state-of-the-art decoding technique to generate a family of quasioptimal community detection algorithms. Second and more important, we show that the Shannon's noisy-channel coding theorem can be invoked to establish a lower bound, here named as decodability bound, for the maximum amount of noise tolerable by an ideal decoder to achieve perfect detection of communities. When computed for well-established synthetic benchmarks, the decodability bound explains accurately the performance achieved by the best community detection algorithms existing on the market, telling us that only little room for their improvement is still potentially left.

Collaborative distance learning: Developing an online learning community

Science.gov (United States)

Stoytcheva, Maria

2017-12-01

The method of collaborative distance learning has been applied for years in a number of distance learning courses, but they are relatively few in foreign language learning. The context of this research is a hybrid distance learning of French for specific purposes, delivered through the platform UNIV-RcT (Strasbourg University), which combines collaborative activities for the realization of a common problem-solving task online. The study focuses on a couple of aspects: on-line interactions carried out in small, tutored groups and the process of community building online. By analyzing the learner's perceptions of community and collaborative learning, we have tried to understand the process of building and maintenance of online learning community and to see to what extent the collaborative distance learning contribute to the development of the competence expectations at the end of the course. The analysis of the results allows us to distinguish the advantages and limitations of this type of e-learning and thus evaluate their pertinence.

Experience Learning and Community Development

Directory of Open Access Journals (Sweden)

Nena Mijoč

1996-12-01

Full Text Available Research in the field of education, carried out in living and working environment, which has undergone so profound changes recently, is of extreme importance. In schools, courses and seminars, one cannot prepare him/herself for the changes as these are often so rapid that it is impossible to foresee them. Therefore, one can only learn by experience. In defining the term 'experience learning', the teoreticians vary greatly. In this paper, experience learning is understood as a process of learning taking part mainly outside the planned educational process and including an active and participative attitude towards environment and people. Original and direct experience can thus serve as a basis for gaining new comprehensions, for planning future activities as well as for a reinterpretation of the past experiences. Let us first mention the basic factors of successful experience learning, such as an individual's character features, possibilities for learning, learning atmosphere and positive stimulations. It has been estimated that local community can increase or decrease the possibilities for experience learning. However, the relation is active in other direction too: the more experience learning bas been asserted in a community, the greater its influence on social and cultural development of the community. On has to bear in mind that well-planned education for local community and stimulating sociocultural animation can facilitate the development of local community.

Mirrored STDP Implements Autoencoder Learning in a Network of Spiking Neurons.

Science.gov (United States)

Burbank, Kendra S

2015-12-01

The autoencoder algorithm is a simple but powerful unsupervised method for training neural networks. Autoencoder networks can learn sparse distributed codes similar to those seen in cortical sensory areas such as visual area V1, but they can also be stacked to learn increasingly abstract representations. Several computational neuroscience models of sensory areas, including Olshausen & Field's Sparse Coding algorithm, can be seen as autoencoder variants, and autoencoders have seen extensive use in the machine learning community. Despite their power and versatility, autoencoders have been difficult to implement in a biologically realistic fashion. The challenges include their need to calculate differences between two neuronal activities and their requirement for learning rules which lead to identical changes at feedforward and feedback connections. Here, we study a biologically realistic network of integrate-and-fire neurons with anatomical connectivity and synaptic plasticity that closely matches that observed in cortical sensory areas. Our choice of synaptic plasticity rules is inspired by recent experimental and theoretical results suggesting that learning at feedback connections may have a different form from learning at feedforward connections, and our results depend critically on this novel choice of plasticity rules. Specifically, we propose that plasticity rules at feedforward versus feedback connections are temporally opposed versions of spike-timing dependent plasticity (STDP), leading to a symmetric combined rule we call Mirrored STDP (mSTDP). We show that with mSTDP, our network follows a learning rule that approximately minimizes an autoencoder loss function. When trained with whitened natural image patches, the learned synaptic weights resemble the receptive fields seen in V1. Our results use realistic synaptic plasticity rules to show that the powerful autoencoder learning algorithm could be within the reach of real biological networks.

Cluster synchronization in community network with hybrid coupling

International Nuclear Information System (INIS)

Yang, Lixin; Jiang, Jun; Liu, Xiaojun

2016-01-01

Highlights: • A community network model with hybrid coupling is proposed. • Control scheme is designed via combining adaptive external coupling strength and feedback control. • The influence of topology structure on synchronization of community network is discussed. - Abstract: A general model of community network with hybrid coupling is proposed in this paper. In the community network model with hybrid coupling, the inner connections are in the same type of coupling within the same community and in different types of coupling in different communities. The connections between different pair of communities are also nonidentical. Cluster synchronization of community network with hybrid coupling is investigated via adaptive couplings control scheme. Effective controllers are designed for constructing an effective control scheme and adjusting automatically the adaptive external coupling strength by taking external coupling strength as adaptive variables on a small fraction of network edges. Moreover, the impact of the topology on the synchronizability of community network is investigated. The numerical results reveal that the number of links between communities and the degree of the connector nodes have significant effects on the synchronization performance.

Experiencing community psychology through community-based learning class projects: reflections from an American University in the Middle East.

Science.gov (United States)

Amer, Mona M; Mohamed, Salma N; Ganzon, Vincent

2013-01-01

Many introductory community psychology courses do not incorporate community-based learning (CBL), and when they do, it is most often in the form of individualized volunteer hours. We present an alternative model for CBL in which the entire class collaborates on an experiential project that promotes community action. We believe that such an approach better embodies the values and methods of the discipline and has a more powerful impact on the students and stakeholders. It may be especially effective in developing countries that do not have an established network of service infrastructures; in such nations the onus is on the teachers and learners of community psychology to contribute to transformative change. In this article practical guidelines are provided by the instructor regarding how to structure and implement this CBL model. Additionally, two students describe how the CBL experience solidified their learning of course concepts and significantly impacted them personally.

Machine learning for identifying botnet network traffic

DEFF Research Database (Denmark)

Stevanovic, Matija; Pedersen, Jens Myrup

2013-01-01

. Due to promise of non-invasive and resilient detection, botnet detection based on network traffic analysis has drawn a special attention of the research community. Furthermore, many authors have turned their attention to the use of machine learning algorithms as the mean of inferring botnet......-related knowledge from the monitored traffic. This paper presents a review of contemporary botnet detection methods that use machine learning as a tool of identifying botnet-related traffic. The main goal of the paper is to provide a comprehensive overview on the field by summarizing current scientific efforts....... The contribution of the paper is three-fold. First, the paper provides a detailed insight on the existing detection methods by investigating which bot-related heuristic were assumed by the detection systems and how different machine learning techniques were adapted in order to capture botnet-related knowledge...

Effects of multiple spreaders in community networks

Science.gov (United States)

Hu, Zhao-Long; Ren, Zhuo-Ming; Yang, Guang-Yong; Liu, Jian-Guo

2014-12-01

Human contact networks exhibit the community structure. Understanding how such community structure affects the epidemic spreading could provide insights for preventing the spreading of epidemics between communities. In this paper, we explore the spreading of multiple spreaders in community networks. A network based on the clustering preferential mechanism is evolved, whose communities are detected by the Girvan-Newman (GN) algorithm. We investigate the spreading effectiveness by selecting the nodes as spreaders in the following ways: nodes with the largest degree in each community (community hubs), the same number of nodes with the largest degree from the global network (global large-degree) and randomly selected one node within each community (community random). The experimental results on the SIR model show that the spreading effectiveness based on the global large-degree and community hubs methods is the same in the early stage of the infection and the method of community random is the worst. However, when the infection rate exceeds the critical value, the global large-degree method embodies the worst spreading effectiveness. Furthermore, the discrepancy of effectiveness for the three methods will decrease as the infection rate increases. Therefore, we should immunize the hubs in each community rather than those hubs in the global network to prevent the outbreak of epidemics.

Social Interaction in Learning Networks

NARCIS (Netherlands)

Sloep, Peter

2009-01-01

The original publication is available from www.springerlink.com. Sloep, P. (2009). Social Interaction in Learning Networks. In R. Koper (Ed.), Learning Network Services for Professional Development (pp 13-15). Berlin, Germany: Springer Verlag.

Building global learning communities

Directory of Open Access Journals (Sweden)

Averill Gordon

2013-09-01

Full Text Available Within the background where education is increasingly driven by the economies of scale and research funding, we propose an alternative online open and connected framework (OOC for building global learning communities using mobile social media. We critique a three year action research case study involving building collaborative global learning communities around a community of practice of learning researchers and practitioners. The results include the development of a framework for utilising mobile social media to support collaborative curriculum development across international boundaries. We conclude that this framework is potentially transferrable to a range of educational contexts where the focus is upon student-generated mobile social media projects.

New designing of E-Learning systems with using network learning

OpenAIRE

Malayeri, Amin Daneshmand; Abdollahi, Jalal

2010-01-01

One of the most applied learning in virtual spaces is using E-Learning systems. Some E-Learning methodologies has been introduced, but the main subject is the most positive feedback from E-Learning systems. In this paper, we introduce a new methodology of E-Learning systems entitle "Network Learning" with review of another aspects of E-Learning systems. Also, we present benefits and advantages of using these systems in educating and fast learning programs. Network Learning can be programmable...

Frameworks for Understanding the Nature of Interactions, Networking, and Community in a Social Networking Site for Academic Practice

Directory of Open Access Journals (Sweden)

Grainne Conole

2011-03-01

Full Text Available This paper describes a new social networking site, Cloudworks, which has been developed to enable discussion and sharing of learning and teaching ideas/designs and to promote reflective academic practice. The site aims to foster new forms of social and participatory practices (peer critiquing, sharing, user-generated content, aggregation, and personalisation within an educational context. One of the key challenges in the development of the site has been to understand the user interactions and the changing patterns of user behaviour as it evolves. The paper explores the extent to which four frameworks that have been used in researching networked learning contexts can provide insights into the patterns of user behaviour that we see in Cloudworks. The paper considers this within the current debate about the new types of interactions, networking, and community being observed as users adapt to and appropriate new technologies.

Toward a Social Approach to Learning in Community Service Learning

Science.gov (United States)

Cooks, Leda; Scharrer, Erica; Paredes, Mari Castaneda

2004-01-01

The authors describe a social approach to learning in community service learning that extends the contributions of three theoretical bodies of scholarship on learning: social constructionism, critical pedagogy, and community service learning. Building on the assumptions about learning described in each of these areas, engagement, identity, and…

Network communities within and across borders.

Science.gov (United States)

Cerina, Federica; Chessa, Alessandro; Pammolli, Fabio; Riccaboni, Massimo

2014-04-01

We investigate the impact of borders on the topology of spatially embedded networks. Indeed territorial subdivisions and geographical borders significantly hamper the geographical span of networks thus playing a key role in the formation of network communities. This is especially important in scientific and technological policy-making, highlighting the interplay between pressure for the internationalization to lead towards a global innovation system and the administrative borders imposed by the national and regional institutions. In this study we introduce an outreach index to quantify the impact of borders on the community structure and apply it to the case of the European and US patent co-inventors networks. We find that (a) the US connectivity decays as a power of distance, whereas we observe a faster exponential decay for Europe; (b) European network communities essentially correspond to nations and contiguous regions while US communities span multiple states across the whole country without any characteristic geographic scale. We confirm our findings by means of a set of simulations aimed at exploring the relationship between different patterns of cross-border community structures and the outreach index.

Integrating a social network group with a 3D collaborative learning environment

NARCIS (Netherlands)

Pourmirza, S.; Gardner, M.; Callaghan, V; Augusto, J.C.; Zhang, T.

2014-01-01

Although extensive research has been carried out on virtual learning environments and the role of groups and communities in social networks, few studies exist which adequately cover the relationship between these two domains. In this paper, the authors demonstrate the effectiveness of integrating

The Learning Community.

Science.gov (United States)

Boo, Mary Richardson; Decker, Larry E.

This guide to community education offers strategies and suggestions for responding to the call for more community involvement in partnership efforts that will benefit education and society. First, a brief introduction summarizes the philosophy of community education, defining it as a belief that learning is lifelong and that self-help efforts…

The development of a network for community-based obesity prevention: the CO-OPS Collaboration

Science.gov (United States)

2011-01-01

Background Community-based interventions are a promising approach and an important component of a comprehensive response to obesity. In this paper we describe the Collaboration of COmmunity-based Obesity Prevention Sites (CO-OPS Collaboration) in Australia as an example of a collaborative network to enhance the quality and quantity of obesity prevention action at the community level. The core aims of the CO-OPS Collaboration are to: identify and analyse the lessons learned from a range of community-based initiatives aimed at tackling obesity, and; to identify the elements that make community-based obesity prevention initiatives successful and share the knowledge gained with other communities. Methods Key activities of the collaboration to date have included the development of a set of Best Practice Principles and knowledge translation and exchange activities to promote the application (or use) of evidence, evaluation and analysis in practice. Results The establishment of the CO-OPS Collaboration is a significant step toward strengthening action in this area, by bringing together research, practice and policy expertise to promote best practice, high quality evaluation and knowledge translation and exchange. Future development of the network should include facilitation of further evidence generation and translation drawing from process, impact and outcome evaluation of existing community-based interventions. Conclusions The lessons presented in this paper may help other networks like CO-OPS as they emerge around the globe. It is important that networks integrate with each other and share the experience of creating these networks. PMID:21349185

A Review of Machine Learning and Data Mining Approaches for Business Applications in Social Networks

OpenAIRE

Evis Trandafili; Marenglen Biba

2013-01-01

Social networks have an outstanding marketing value and developing data mining methods for viral marketing is a hot topic in the research community. However, most social networks remain impossible to be fully analyzed and understood due to prohibiting sizes and the incapability of traditional machine learning and data mining approaches to deal with the new dimension in the learning process related to the large-scale environment where the data are produced. On one hand, the birth and evolution...

Weighted Evolving Networks with Self-organized Communities

International Nuclear Information System (INIS)

Xie Zhou; Wang Xiaofan; Li Xiang

2008-01-01

In order to describe the self-organization of communities in the evolution of weighted networks, we propose a new evolving model for weighted community-structured networks with the preferential mechanisms functioned in different levels according to community sizes and node strengths, respectively. Theoretical analyses and numerical simulations show that our model captures power-law distributions of community sizes, node strengths, and link weights, with tunable exponents of ν ≥ 1, γ > 2, and α > 2, respectively, sharing large clustering coefficients and scaling clustering spectra, and covering the range from disassortative networks to assortative networks. Finally, we apply our new model to the scientific co-authorship networks with both their weighted and unweighted datasets to verify its effectiveness

Finding local communities in protein networks.

Science.gov (United States)

Voevodski, Konstantin; Teng, Shang-Hua; Xia, Yu

2009-09-18

Protein-protein interactions (PPIs) play fundamental roles in nearly all biological processes, and provide major insights into the inner workings of cells. A vast amount of PPI data for various organisms is available from BioGRID and other sources. The identification of communities in PPI networks is of great interest because they often reveal previously unknown functional ties between proteins. A large number of global clustering algorithms have been applied to protein networks, where the entire network is partitioned into clusters. Here we take a different approach by looking for local communities in PPI networks. We develop a tool, named Local Protein Community Finder, which quickly finds a community close to a queried protein in any network available from BioGRID or specified by the user. Our tool uses two new local clustering algorithms Nibble and PageRank-Nibble, which look for a good cluster among the most popular destinations of a short random walk from the queried vertex. The quality of a cluster is determined by proportion of outgoing edges, known as conductance, which is a relative measure particularly useful in undersampled networks. We show that the two local clustering algorithms find communities that not only form excellent clusters, but are also likely to be biologically relevant functional components. We compare the performance of Nibble and PageRank-Nibble to other popular and effective graph partitioning algorithms, and show that they find better clusters in the graph. Moreover, Nibble and PageRank-Nibble find communities that are more functionally coherent. The Local Protein Community Finder, accessible at http://xialab.bu.edu/resources/lpcf, allows the user to quickly find a high-quality community close to a queried protein in any network available from BioGRID or specified by the user. We show that the communities found by our tool form good clusters and are functionally coherent, making our application useful for biologists who wish to

Finding local communities in protein networks

Directory of Open Access Journals (Sweden)

Teng Shang-Hua

2009-09-01

Full Text Available Abstract Background Protein-protein interactions (PPIs play fundamental roles in nearly all biological processes, and provide major insights into the inner workings of cells. A vast amount of PPI data for various organisms is available from BioGRID and other sources. The identification of communities in PPI networks is of great interest because they often reveal previously unknown functional ties between proteins. A large number of global clustering algorithms have been applied to protein networks, where the entire network is partitioned into clusters. Here we take a different approach by looking for local communities in PPI networks. Results We develop a tool, named Local Protein Community Finder, which quickly finds a community close to a queried protein in any network available from BioGRID or specified by the user. Our tool uses two new local clustering algorithms Nibble and PageRank-Nibble, which look for a good cluster among the most popular destinations of a short random walk from the queried vertex. The quality of a cluster is determined by proportion of outgoing edges, known as conductance, which is a relative measure particularly useful in undersampled networks. We show that the two local clustering algorithms find communities that not only form excellent clusters, but are also likely to be biologically relevant functional components. We compare the performance of Nibble and PageRank-Nibble to other popular and effective graph partitioning algorithms, and show that they find better clusters in the graph. Moreover, Nibble and PageRank-Nibble find communities that are more functionally coherent. Conclusion The Local Protein Community Finder, accessible at http://xialab.bu.edu/resources/lpcf, allows the user to quickly find a high-quality community close to a queried protein in any network available from BioGRID or specified by the user. We show that the communities found by our tool form good clusters and are functionally coherent

Social Networking Technologies as Vehicles of Support for Women in Learning Communities

Science.gov (United States)

Burgess, Kimberly R.

2009-01-01

Women have long since used social networking as a means of coping with their struggles, educating and empowering themselves, engaging in broader social movements, and building international advocacy. Internet communities that are designed and facilitated to be inclusive of women's experiences can be important social spaces where women feel…

School-community learning partnerships for sustainability: Recommended best practice and reality

Science.gov (United States)

Wheeler, Leone; Guevara, Jose Roberto; Smith, Jodi-Anne

2018-05-01

Effective partnerships across different stakeholders are essential to the collaboration required for learning cities to contribute to sustainable development. Through partnerships, formal educational institutions, such as schools and universities, play a vital role in establishing and sustaining learning cities, often by facilitating the meaningful participation of different local community members. The research presented in this article examines the characteristics of effective school-community partnerships in the literature and compares it to the results of a three-year research study which examined 16 case studies of school-community partnerships in the state of Victoria in Australia. Using participatory action research, the researchers identified four approaches to implementing partnerships for sustainability, explored challenges to achieving an idealised partnership, and made recommendations for establishing successful partnership networks. The researchers propose that partnerships be viewed as a dynamic resource rather than merely a transactional arrangement that addresses the identified challenges of time, funding, skills and personnel. Furthermore, the use of "partnership brokers", such as local government or non-government organisations, is recommended to expand the current school-centred approach to partnerships. These insights aim to contribute to providing quality education and lifelong learning through partnerships - outcomes crucial for establishing and sustaining learning cities.

Project team formation support for self-directed learners in social learning networks

NARCIS (Netherlands)

Spoelstra, Howard; Van Rosmalen, Peter; Sloep, Peter

2012-01-01

Spoelstra, H., Van Rosmalen, P., & Sloep, P. B. (2012). Project team formation support for self-directed learners in social learning networks. In P. Kommers, P. Isaias, & N. Bessis (Eds.), Proceedings of the IADIS International Conference on Web Based Communities and Social Media (ICWBC & SM 2012)

Assessing Community Informatics: A Review of Methodological Approaches for Evaluating Community Networks and Community Technology Centers.

Science.gov (United States)

O'Neil, Dara

2002-01-01

Analyzes the emerging community informatics evaluation literature to develop an understanding of the indicators used to gauge project impacts in community networks and community technology centers. The study finds that community networks and community technology center assessments fall into five key areas: strong democracy; social capital;…

Effects of Hierarchical Levels on Social Network Structures within Communities of Learning

Science.gov (United States)

Rehm, Martin; Gijselaers, Wim; Segers, Mien

2014-01-01

Facilitating an interpersonal knowledge transfer among employees constitutes a key building block in setting up organizational training initiatives. With practitioners and researchers looking for innovative training methods, online Communities of Learning (CoL) have been promoted as a promising methodology to foster this kind of transfer. However,…

Does Gender Matter? Collaborative Learning in a Virtual Corporate Community of Practice

Science.gov (United States)

Tomcsik, Rachel E.

2010-01-01

The purpose of this study was to investigate how gender identity construction in virtuality and actuality affect collaborative learning in a corporate community of practice. As part of a virtual ethnographic design, participants were employees from a major American corporation who were interested specifically in social networking applications. The…

Merging social networking environments and formal learning environments to support and facilitate interprofessional instruction.

Science.gov (United States)

King, Sharla; Greidanus, Elaine; Carbonaro, Michael; Drummond, Jane; Patterson, Steven

2009-04-28

This study describes the redesign of an interprofessional team development course for health science students. A theoretical model is hypothesized as a framework for the redesign process, consisting of two themes: 1) the increasing trend among post-secondary students to participate in social networking (e.g., Facebook, Second Life) and 2) the need for healthcare educators to provide interprofessional training that results in effective communities of practice and better patient care. The redesign focused on increasing the relevance of the course through the integration of custom-designed technology to facilitate social networking during their interprofessional education. Results suggest that students in an educationally structured social networking environment can be guided to join learning communities quickly and access course materials. More research and implementation work is required to effectively develop interprofessional health sciences communities in a combined face-to-face and on-line social networking context.

Emergence of communities and diversity in social networks.

Science.gov (United States)

Han, Xiao; Cao, Shinan; Shen, Zhesi; Zhang, Boyu; Wang, Wen-Xu; Cressman, Ross; Stanley, H Eugene

2017-03-14

Communities are common in complex networks and play a significant role in the functioning of social, biological, economic, and technological systems. Despite widespread interest in detecting community structures in complex networks and exploring the effect of communities on collective dynamics, a deep understanding of the emergence and prevalence of communities in social networks is still lacking. Addressing this fundamental problem is of paramount importance in understanding, predicting, and controlling a variety of collective behaviors in society. An elusive question is how communities with common internal properties arise in social networks with great individual diversity. Here, we answer this question using the ultimatum game, which has been a paradigm for characterizing altruism and fairness. We experimentally show that stable local communities with different internal agreements emerge spontaneously and induce social diversity into networks, which is in sharp contrast to populations with random interactions. Diverse communities and social norms come from the interaction between responders with inherent heterogeneous demands and rational proposers via local connections, where the former eventually become the community leaders. This result indicates that networks are significant in the emergence and stabilization of communities and social diversity. Our experimental results also provide valuable information about strategies for developing network models and theories of evolutionary games and social dynamics.

Network Analysis in Community Psychology: Looking Back, Looking Forward

OpenAIRE

Neal, Zachary P.; Neal, Jennifer Watling

2017-01-01

Highlights Network analysis is ideally suited for community psychology research because it focuses on context. Use of network analysis in community psychology is growing. Network analysis in community psychology has employed some potentially problematic practices. Recommended practices are identified to improve network analysis in community psychology.

Fastest learning in small-world neural networks

International Nuclear Information System (INIS)

Simard, D.; Nadeau, L.; Kroeger, H.

2005-01-01

We investigate supervised learning in neural networks. We consider a multi-layered feed-forward network with back propagation. We find that the network of small-world connectivity reduces the learning error and learning time when compared to the networks of regular or random connectivity. Our study has potential applications in the domain of data-mining, image processing, speech recognition, and pattern recognition

A Professional Learning Community Journey

Directory of Open Access Journals (Sweden)

Diana Maliszewski

2008-06-01

Full Text Available Four teachers (three classroom teachers and a teacher-librarian explain how their school applied a professional learning community framework to its operational practices. They discuss the process, the benefits, and the challenges of professional learning communities.

The Integration of Personal Learning Environments & Open Network Learning Environments

Science.gov (United States)

Tu, Chih-Hsiung; Sujo-Montes, Laura; Yen, Cherng-Jyh; Chan, Junn-Yih; Blocher, Michael

2012-01-01

Learning management systems traditionally provide structures to guide online learners to achieve their learning goals. Web 2.0 technology empowers learners to create, share, and organize their personal learning environments in open network environments; and allows learners to engage in social networking and collaborating activities. Advanced…

Community Core Evolution in Mobile Social Networks

Directory of Open Access Journals (Sweden)

Hao Xu

2013-01-01

Full Text Available Community detection in social networks attracts a lot of attention in the recent years. Existing methods always depict the relationship of two nodes using the temporary connection. However, these temporary connections cannot be fully recognized as the real relationships when the history connections among nodes are considered. For example, a casual visit in Facebook cannot be seen as an establishment of friendship. Hence, our question is the following: how to cluster the real friends in mobile social networks? In this paper, we study the problem of detecting the stable community core in mobile social networks. The cumulative stable contact is proposed to depict the relationship among nodes. The whole process is divided into timestamps. Nodes and their connections can be added or removed at each timestamp, and historical contacts are considered when detecting the community core. Also, community cores can be tracked through the incremental computing, which can help to recognize the evolving of community structure. Empirical studies on real-world social networks demonstrate that our proposed method can effectively detect stable community cores in mobile social networks.

Community core evolution in mobile social networks.

Science.gov (United States)

Xu, Hao; Xiao, Weidong; Tang, Daquan; Tang, Jiuyang; Wang, Zhenwen

2013-01-01

Community detection in social networks attracts a lot of attention in the recent years. Existing methods always depict the relationship of two nodes using the temporary connection. However, these temporary connections cannot be fully recognized as the real relationships when the history connections among nodes are considered. For example, a casual visit in Facebook cannot be seen as an establishment of friendship. Hence, our question is the following: how to cluster the real friends in mobile social networks? In this paper, we study the problem of detecting the stable community core in mobile social networks. The cumulative stable contact is proposed to depict the relationship among nodes. The whole process is divided into timestamps. Nodes and their connections can be added or removed at each timestamp, and historical contacts are considered when detecting the community core. Also, community cores can be tracked through the incremental computing, which can help to recognize the evolving of community structure. Empirical studies on real-world social networks demonstrate that our proposed method can effectively detect stable community cores in mobile social networks.

Community Health Global Network and Sustainable Development

Directory of Open Access Journals (Sweden)

Rebekah Young

2016-01-01

Full Text Available With the achievements, failures and passing of the Millennium Development Goals (MDG, the world has turned its eyes to the Sustainable Development Goals (SDG, designed to foster sustainable social, economic and environmental development over the next 15 years.(1 Community-led initiatives are increasingly being recognised as playing a key role in realising sustainable community development and in the aspirations of universal healthcare.(2 In many parts of the world, faith-based organisations are some of the main players in community-led development and health care.(3 Community Health Global Network (CHGN creates links between organisations, with the purpose being to encourage communities to recognise their assets and abilities, identify shared concerns and discover solutions together, in order to define and lead their futures in sustainable ways.(4 CHGN has facilitated the development of collaborative groups of health and development initiatives called ‘Clusters’ in several countries including India, Bangladesh, Kenya, Tanzania, Zambia and Myanmar. In March 2016 these Clusters met together in an International Forum, to share learnings, experiences, challenges, achievements and to encourage one another. Discussions held throughout the forum suggest that the CHGN model is helping to promote effective, sustainable development and health care provision on both a local and a global scale.

Learning Networks for Professional Development & Lifelong Learning

NARCIS (Netherlands)

Sloep, Peter

2009-01-01

Sloep, P. B. (2009). Learning Networks for Professional Development & Lifelong Learning. Presentation at a NeLLL seminar with Etienne Wenger held at the Open Universiteit Nederland. September, 10, 2009, Heerlen, The Netherlands.

Creating a Community of Practice: Lessons Learned from the Center for Astronomy Education (Invited)

Science.gov (United States)

Brissenden, G.

2009-12-01

The Center for Astronomy Education (CAE) is devoted to improving teaching and learning in Astro 101. To accomplish this, a vital part of CAE is our broader community of practice which includes over 1000 instructors, graduate and undergraduate students, and postdocs. It is this greater community of practice that supports each other, helps, and learns from each other beyond what would be possible without it. As our community of practice has grown, we at CAE have learned many lessons about how different facets of CAE can best be used to promote and support our community both as a whole and for individual members. We will discuss the various facets of CAE, such as our online discussion group Astrolrner@CAE (http://astronomy101.jpl.nasa.gov/discussion) and its Guest Moderator program, our CAE Regional Teaching Exchange Coordinator program, our CAE Workshop Presenter Apprenticeship Training program, our online This Month’s Teaching Strategy, monthly newsletters, and various types of socializing and networking sessions we hold at national meetings. But more importantly, we will discuss the lessons we’ve learned about what does and does not work in building community within each of these facets.

Networked Community Change: Understanding Community Systems Change through the Lens of Social Network Analysis.

Science.gov (United States)

Lawlor, Jennifer A; Neal, Zachary P

2016-06-01

Addressing complex problems in communities has become a key area of focus in recent years (Kania & Kramer, 2013, Stanford Social Innovation Review). Building on existing approaches to understanding and addressing problems, such as action research, several new approaches have emerged that shift the way communities solve problems (e.g., Burns, 2007, Systemic Action Research; Foth, 2006, Action Research, 4, 205; Kania & Kramer, 2011, Stanford Social Innovation Review, 1, 36). Seeking to bring clarity to the emerging literature on community change strategies, this article identifies the common features of the most widespread community change strategies and explores the conditions under which such strategies have the potential to be effective. We identify and describe five common features among the approaches to change. Then, using an agent-based model, we simulate network-building behavior among stakeholders participating in community change efforts using these approaches. We find that the emergent stakeholder networks are efficient when the processes are implemented under ideal conditions. © Society for Community Research and Action 2016.

Edmodo social learning network for elementary school mathematics learning

Science.gov (United States)

Ariani, Y.; Helsa, Y.; Ahmad, S.; Prahmana, RCI

2017-12-01

A developed instructional media can be as printed media, visual media, audio media, and multimedia. The development of instructional media can also take advantage of technological development by utilizing Edmodo social network. This research aims to develop a digital classroom learning model using Edmodo social learning network for elementary school mathematics learning which is practical, valid and effective in order to improve the quality of learning activities. The result of this research showed that the prototype of mathematics learning device for elementary school students using Edmodo was in good category. There were 72% of students passed the assessment as a result of Edmodo learning. Edmodo has become a promising way to engage students in a collaborative learning process.

Network Community Detection on Metric Space

Directory of Open Access Journals (Sweden)

Suman Saha

2015-08-01

Full Text Available Community detection in a complex network is an important problem of much interest in recent years. In general, a community detection algorithm chooses an objective function and captures the communities of the network by optimizing the objective function, and then, one uses various heuristics to solve the optimization problem to extract the interesting communities for the user. In this article, we demonstrate the procedure to transform a graph into points of a metric space and develop the methods of community detection with the help of a metric defined for a pair of points. We have also studied and analyzed the community structure of the network therein. The results obtained with our approach are very competitive with most of the well-known algorithms in the literature, and this is justified over the large collection of datasets. On the other hand, it can be observed that time taken by our algorithm is quite less compared to other methods and justifies the theoretical findings.

Entropy Learning in Neural Network

Directory of Open Access Journals (Sweden)

Geok See Ng

2017-12-01

Full Text Available In this paper, entropy term is used in the learning phase of a neural network.  As learning progresses, more hidden nodes get into saturation.  The early creation of such hidden nodes may impair generalisation.  Hence entropy approach is proposed to dampen the early creation of such nodes.  The entropy learning also helps to increase the importance of relevant nodes while dampening the less important nodes.  At the end of learning, the less important nodes can then be eliminated to reduce the memory requirements of the neural network.

Phase synchronization on small-world networks with community structure

International Nuclear Information System (INIS)

Xiao-Hua, Wang; Li-Cheng, Jiao; Jian-She, Wu

2010-01-01

In this paper, we propose a simple model that can generate small-world network with community structure. The network is introduced as a tunable community organization with parameter r, which is directly measured by the ratio of inter- to intra-community connectivity, and a smaller r corresponds to a stronger community structure. The structure properties, including the degree distribution, clustering, the communication efficiency and modularity are also analysed for the network. In addition, by using the Kuramoto model, we investigated the phase synchronization on this network, and found that increasing the fuzziness of community structure will markedly enhance the network synchronizability; however, in an abnormal region (r ≤ 0.001), the network has even worse synchronizability than the case of isolated communities (r = 0). Furthermore, this network exhibits a remarkable synchronization behaviour in topological scales: the oscillators of high densely interconnected communities synchronize more easily, and more rapidly than the whole network. (general)

Using smart mobile devices in social-network-based health education practice: a learning behavior analysis.

Science.gov (United States)

Wu, Ting-Ting

2014-06-01

Virtual communities provide numerous resources, immediate feedback, and information sharing, enabling people to rapidly acquire information and knowledge and supporting diverse applications that facilitate interpersonal interactions, communication, and sharing. Moreover, incorporating highly mobile and convenient devices into practice-based courses can be advantageous in learning situations. Therefore, in this study, a tablet PC and Google+ were introduced to a health education practice course to elucidate satisfaction of learning module and conditions and analyze the sequence and frequency of learning behaviors during the social-network-based learning process. According to the analytical results, social networks can improve interaction among peers and between educators and students, particularly when these networks are used to search for data, post articles, engage in discussions, and communicate. In addition, most nursing students and nursing educators expressed a positive attitude and satisfaction toward these innovative teaching methods, and looked forward to continuing the use of this learning approach. Copyright © 2014 Elsevier Ltd. All rights reserved.

A novel community detection method in bipartite networks

Science.gov (United States)

Zhou, Cangqi; Feng, Liang; Zhao, Qianchuan

2018-02-01

Community structure is a common and important feature in many complex networks, including bipartite networks, which are used as a standard model for many empirical networks comprised of two types of nodes. In this paper, we propose a two-stage method for detecting community structure in bipartite networks. Firstly, we extend the widely-used Louvain algorithm to bipartite networks. The effectiveness and efficiency of the Louvain algorithm have been proved by many applications. However, there lacks a Louvain-like algorithm specially modified for bipartite networks. Based on bipartite modularity, a measure that extends unipartite modularity and that quantifies the strength of partitions in bipartite networks, we fill the gap by developing the Bi-Louvain algorithm that iteratively groups the nodes in each part by turns. This algorithm in bipartite networks often produces a balanced network structure with equal numbers of two types of nodes. Secondly, for the balanced network yielded by the first algorithm, we use an agglomerative clustering method to further cluster the network. We demonstrate that the calculation of the gain of modularity of each aggregation, and the operation of joining two communities can be compactly calculated by matrix operations for all pairs of communities simultaneously. At last, a complete hierarchical community structure is unfolded. We apply our method to two benchmark data sets and a large-scale data set from an e-commerce company, showing that it effectively identifies community structure in bipartite networks.

Social Networks and the Building of Learning Communities: An Experimental Study of a Social MOOC

Science.gov (United States)

de Lima, Mariana; Zorrilla, Marta

2017-01-01

This study aimed to analyze the student's behaviour in relation to their degree of commitment, participation, and contribution in a MOOC based on a social learning approach. Interaction data was collected on the learning platform and in social networks, both of which were used in the third edition of a social MOOC course. This data was then…

Cooperation in the prisoner's dilemma game on tunable community networks

Science.gov (United States)

Liu, Penghui; Liu, Jing

2017-04-01

Community networks have attracted lots of attention as they widely exist in the real world and are essential to study properties of networks. As the game theory illustrates the competitive relationship among individuals, studying the iterated prisoner's dilemma games (PDG) on community networks is meaningful. In this paper, we focus on investigating the relationship between the cooperation level of community networks and that of their communities in the prisoner's dilemma games. With this purpose in mind, a type of tunable community networks whose communities inherit not only the scale-free property, but also the characteristic of adjustable cooperation level of Holme and Kim (HK) networks is designed. Both uniform and non-uniform community networks are investigated. We find out that cooperation enhancement of communities can improve the cooperation level of the whole networks. Moreover, simulation results indicate that a large community is a better choice than a small community to improve the cooperation level of the whole networks. Thus, improving the cooperation level of community networks can be divided into a number of sub-problems targeting at improving the cooperation level of individual communities, which can save the computation cost and deal with the problem of improving the cooperation level of huge community networks. Moreover, as the larger community is a better choice, it is reasonable to start with large communities, according to the greedy strategy when the number of nodes can participate in the enhancement is limited.

Community Based Networks and 5G

DEFF Research Database (Denmark)

Williams, Idongesit

2016-01-01

The deployment of previous wireless standards has provided more benefits for urban dwellers than rural dwellers. 5G deployment may not be different. This paper identifies that Community Based Networks as carriers that deserve recognition as potential 5G providers may change this. The argument....... The findings indicate that 5G connectivity can be extended to rural areas by these networks, via heterogenous networks. Hence the delivery of 5G data rates delivery via Wireless WAN in rural areas can be achieved by utilizing the causal factors of the identified models for Community Based Networks....

Brand communities embedded in social networks.

Science.gov (United States)

Zaglia, Melanie E

2013-02-01

Brand communities represent highly valuable marketing, innovation management, and customer relationship management tools. However, applying successful marketing strategies today, and in the future, also means exploring and seizing the unprecedented opportunities of social network environments. This study combines these two social phenomena which have largely been researched separately, and aims to investigate the existence, functionality and different types of brand communities within social networks. The netnographic approach yields strong evidence of this existence; leading to a better understanding of such embedded brand communities, their peculiarities, and motivational drivers for participation; therefore the findings contribute to theory by combining two separate research streams. Due to the advantages of social networks, brand management is now able to implement brand communities with less time and financial effort; however, choosing the appropriate brand community type, cultivating consumers' interaction, and staying tuned to this social engagement are critical factors to gain anticipated brand outcomes.

On the Importance of Personal Profiles to Enhance Social Interaction in Learning Networks

NARCIS (Netherlands)

Berlanga, Adriana

2008-01-01

Berlanga, A. J., Bitter-Rijpkema, M. E., Brouns F., & Sloep, P. B. (2008). On the Importance of Personal Profiles to Enhance Social Interaction in Learning Networks. Presented at the IADIS International Conference on Web Based Communities 2008. July, 24-26, 2008, Amsterdam, The Netherlands.

Quantitative learning strategies based on word networks

Science.gov (United States)

Zhao, Yue-Tian-Yi; Jia, Zi-Yang; Tang, Yong; Xiong, Jason Jie; Zhang, Yi-Cheng

2018-02-01

Learning English requires a considerable effort, but the way that vocabulary is introduced in textbooks is not optimized for learning efficiency. With the increasing population of English learners, learning process optimization will have significant impact and improvement towards English learning and teaching. The recent developments of big data analysis and complex network science provide additional opportunities to design and further investigate the strategies in English learning. In this paper, quantitative English learning strategies based on word network and word usage information are proposed. The strategies integrate the words frequency with topological structural information. By analyzing the influence of connected learned words, the learning weights for the unlearned words and dynamically updating of the network are studied and analyzed. The results suggest that quantitative strategies significantly improve learning efficiency while maintaining effectiveness. Especially, the optimized-weight-first strategy and segmented strategies outperform other strategies. The results provide opportunities for researchers and practitioners to reconsider the way of English teaching and designing vocabularies quantitatively by balancing the efficiency and learning costs based on the word network.

Network Analysis in Community Psychology: Looking Back, Looking Forward.

Science.gov (United States)

Neal, Zachary P; Neal, Jennifer Watling

2017-09-01

Network analysis holds promise for community psychology given the field's aim to understand the interplay between individuals and their social contexts. Indeed, because network analysis focuses explicitly on patterns of relationships between actors, its theories and methods are inherently extra-individual in nature and particularly well suited to characterizing social contexts. But, to what extent has community psychology taken advantage of this network analysis as a tool for capturing context? To answer these questions, this study provides a review of the use network analysis in articles published in American Journal of Community Psychology. Looking back, we describe and summarize the ways that network analysis has been employed in community psychology research to understand the range of ways community psychologists have found the technique helpful. Looking forward and paying particular attention to analytic issues identified in past applications, we provide some recommendations drawn from the network analysis literature to facilitate future applications of network analysis in community psychology. © 2017 The Authors. American Journal of Community Psychology published by Wiley Periodicals, Inc. on behalf of Society for Community Research and Action.

Distributed Extreme Learning Machine for Nonlinear Learning over Network

Directory of Open Access Journals (Sweden)

Songyan Huang

2015-02-01

Full Text Available Distributed data collection and analysis over a network are ubiquitous, especially over a wireless sensor network (WSN. To our knowledge, the data model used in most of the distributed algorithms is linear. However, in real applications, the linearity of systems is not always guaranteed. In nonlinear cases, the single hidden layer feedforward neural network (SLFN with radial basis function (RBF hidden neurons has the ability to approximate any continuous functions and, thus, may be used as the nonlinear learning system. However, confined by the communication cost, using the distributed version of the conventional algorithms to train the neural network directly is usually prohibited. Fortunately, based on the theorems provided in the extreme learning machine (ELM literature, we only need to compute the output weights of the SLFN. Computing the output weights itself is a linear learning problem, although the input-output mapping of the overall SLFN is still nonlinear. Using the distributed algorithmto cooperatively compute the output weights of the SLFN, we obtain a distributed extreme learning machine (dELM for nonlinear learning in this paper. This dELM is applied to the regression problem and classification problem to demonstrate its effectiveness and advantages.

Language Choice & Global Learning Networks

Directory of Open Access Journals (Sweden)

Dennis Sayers

1995-05-01

Full Text Available How can other languages be used in conjunction with English to further intercultural and multilingual learning when teachers and students participate in computer-based global learning networks? Two portraits are presented of multilingual activities in the Orillas and I*EARN learning networks, and are discussed as examples of the principal modalities of communication employed in networking projects between distant classes. Next, an important historical precedent --the social controversy which accompanied the introduction of telephone technology at the end of the last century-- is examined in terms of its implications for language choice in contemporary classroom telecomputing projects. Finally, recommendations are offered to guide decision making concerning the role of language choice in promoting collaborative critical inquiry.

Learning Networks Distributed Environment

NARCIS (Netherlands)

Martens, Harrie; Vogten, Hubert; Koper, Rob; Tattersall, Colin; Van Rosmalen, Peter; Sloep, Peter; Van Bruggen, Jan; Spoelstra, Howard

2005-01-01

Learning Networks Distributed Environment is a prototype of an architecture that allows the sharing and modification of learning materials through a number of transport protocols. The prototype implements a p2p protcol using JXTA.

Intelligent sensor networks the integration of sensor networks, signal processing and machine learning

CERN Document Server

Hu, Fei

2012-01-01

Although governments worldwide have invested significantly in intelligent sensor network research and applications, few books cover intelligent sensor networks from a machine learning and signal processing perspective. Filling this void, Intelligent Sensor Networks: The Integration of Sensor Networks, Signal Processing and Machine Learning focuses on the close integration of sensing, networking, and smart signal processing via machine learning. Based on the world-class research of award-winning authors, the book provides a firm grounding in the fundamentals of intelligent sensor networks, incl

Epidemics in adaptive networks with community structure

Science.gov (United States)

Shaw, Leah; Tunc, Ilker

2010-03-01

Models for epidemic spread on static social networks do not account for changes in individuals' social interactions. Recent studies of adaptive networks have modeled avoidance behavior, as non-infected individuals try to avoid contact with infectives. Such models have not generally included realistic social structure. Here we study epidemic spread on an adaptive network with community structure. We model the effect of heterogeneous communities on infection levels and epidemic extinction. We also show how an epidemic can alter the community structure.

On the importance of personal profiles to enhance social interaction in Learning Networks

NARCIS (Netherlands)

Berlanga, Adriana; Bitter-Rijpkema, Marlies; Brouns, Francis; Sloep, Peter

2008-01-01

Berlanga, A. J., Bitter-Rijpkema, M., Brouns F., & Sloep, P.B. (2008). On the importance of personal profiles to enhance social interaction in Learning Networks. In P. Kommers (Ed.), Proceedings of Web Based Communities Conference (WEBC 2008) (pp. 55-62). July, 24-26, 2008, Amsterdam, The

Homophyly/kinship hypothesis: Natural communities, and predicting in networks

Science.gov (United States)

Li, Angsheng; Li, Jiankou; Pan, Yicheng

2015-02-01

It has been a longstanding challenge to understand natural communities in real world networks. We proposed a community finding algorithm based on fitness of networks, two algorithms for prediction, accurate prediction and confirmation of keywords for papers in the citation network Arxiv HEP-TH (high energy physics theory), and the measures of internal centrality, external de-centrality, internal and external slopes to characterize the structures of communities. We implemented our algorithms on 2 citation and 5 cooperation graphs. Our experiments explored and validated a homophyly/kinship principle of real world networks. The homophyly/kinship principle includes: (1) homophyly is the natural selection in real world networks, similar to Darwin's kinship selection in nature, (2) real world networks consist of natural communities generated by the natural selection of homophyly, (3) most individuals in a natural community share a short list of common attributes, (4) natural communities have an internal centrality (or internal heterogeneity) that a natural community has a few nodes dominating most of the individuals in the community, (5) natural communities have an external de-centrality (or external homogeneity) that external links of a natural community homogeneously distributed in different communities, and (6) natural communities of a given network have typical structures determined by the internal slopes, and have typical patterns of outgoing links determined by external slopes, etc. Our homophyly/kinship principle perfectly matches Darwin's observation that animals from ants to people form social groups in which most individuals work for the common good, and that kinship could encourage altruistic behavior. Our homophyly/kinship principle is the network version of Darwinian theory, and builds a bridge between Darwinian evolution and network science.

Deep Learning Neural Networks and Bayesian Neural Networks in Data Analysis

Directory of Open Access Journals (Sweden)

Chernoded Andrey

2017-01-01

Full Text Available Most of the modern analyses in high energy physics use signal-versus-background classification techniques of machine learning methods and neural networks in particular. Deep learning neural network is the most promising modern technique to separate signal and background and now days can be widely and successfully implemented as a part of physical analysis. In this article we compare Deep learning and Bayesian neural networks application as a classifiers in an instance of top quark analysis.

Community Based Networks and 5G Wi-Fi

DEFF Research Database (Denmark)

Williams, Idongesit

2018-01-01

This paper argues on why Community Based Networks should be recognized as potential 5G providers using 5G Wi-Fi. The argument is hinged on findings in a research to understand why Community Based Networks deploy telecom and Broadband infrastructure. The study was a qualitative study carried out...... inductively using Grounded Theory. Six cases were investigated. Two Community Based Network Mobilization Models were identified. The findings indicate that 5G Wi-Fi deployment by Community Based Networks is possible if policy initiatives and the 5G Wi-Fi standards are developed to facilitate the causal...

Similarity between community structures of different online social networks and its impact on underlying community detection

Science.gov (United States)

Fan, W.; Yeung, K. H.

2015-03-01

As social networking services are popular, many people may register in more than one online social network. In this paper we study a set of users who have accounts of three online social networks: namely Foursquare, Facebook and Twitter. Community structure of this set of users may be reflected in these three online social networks. Therefore, high correlation between these reflections and the underlying community structure may be observed. In this work, community structures are detected in all three online social networks. Also, we investigate the similarity level of community structures across different networks. It is found that they show strong correlation with each other. The similarity between different networks may be helpful to find a community structure close to the underlying one. To verify this, we propose a method to increase the weights of some connections in networks. With this method, new networks are generated to assist community detection. By doing this, value of modularity can be improved and the new community structure match network's natural structure better. In this paper we also show that the detected community structures of online social networks are correlated with users' locations which are identified on Foursquare. This information may also be useful for underlying community detection.

Learning and coding in biological neural networks

Science.gov (United States)

Fiete, Ila Rani

How can large groups of neurons that locally modify their activities learn to collectively perform a desired task? Do studies of learning in small networks tell us anything about learning in the fantastically large collection of neurons that make up a vertebrate brain? What factors do neurons optimize by encoding sensory inputs or motor commands in the way they do? In this thesis I present a collection of four theoretical works: each of the projects was motivated by specific constraints and complexities of biological neural networks, as revealed by experimental studies; together, they aim to partially address some of the central questions of neuroscience posed above. We first study the role of sparse neural activity, as seen in the coding of sequential commands in a premotor area responsible for birdsong. We show that the sparse coding of temporal sequences in the songbird brain can, in a network where the feedforward plastic weights must translate the sparse sequential code into a time-varying muscle code, facilitate learning by minimizing synaptic interference. Next, we propose a biologically plausible synaptic plasticity rule that can perform goal-directed learning in recurrent networks of voltage-based spiking neurons that interact through conductances. Learning is based on the correlation of noisy local activity with a global reward signal; we prove that this rule performs stochastic gradient ascent on the reward. Thus, if the reward signal quantifies network performance on some desired task, the plasticity rule provably drives goal-directed learning in the network. To assess the convergence properties of the learning rule, we compare it with a known example of learning in the brain. Song-learning in finches is a clear example of a learned behavior, with detailed available neurophysiological data. With our learning rule, we train an anatomically accurate model birdsong network that drives a sound source to mimic an actual zebrafinch song. Simulation and

Overlapping community detection in weighted networks via a Bayesian approach

Science.gov (United States)

Chen, Yi; Wang, Xiaolong; Xiang, Xin; Tang, Buzhou; Chen, Qingcai; Fan, Shixi; Bu, Junzhao

2017-02-01

Complex networks as a powerful way to represent complex systems have been widely studied during the past several years. One of the most important tasks of complex network analysis is to detect communities embedded in networks. In the real world, weighted networks are very common and may contain overlapping communities where a node is allowed to belong to multiple communities. In this paper, we propose a novel Bayesian approach, called the Bayesian mixture network (BMN) model, to detect overlapping communities in weighted networks. The advantages of our method are (i) providing soft-partition solutions in weighted networks; (ii) providing soft memberships, which quantify 'how strongly' a node belongs to a community. Experiments on a large number of real and synthetic networks show that our model has the ability in detecting overlapping communities in weighted networks and is competitive with other state-of-the-art models at shedding light on community partition.

A Weighted Evolving Network with Community Size Preferential Attachment

International Nuclear Information System (INIS)

Zhuo Zhiwei; Shan Erfang

2010-01-01

Community structure is an important characteristic in real complex network. It is a network consists of groups of nodes within which links are dense but among which links are sparse. In this paper, the evolving network include node, link and community growth and we apply the community size preferential attachment and strength preferential attachment to a growing weighted network model and utilize weight assigning mechanism from BBV model. The resulting network reflects the intrinsic community structure with generalized power-law distributions of nodes' degrees and strengths.

Fast unfolding of communities in large networks

International Nuclear Information System (INIS)

Blondel, Vincent D; Guillaume, Jean-Loup; Lambiotte, Renaud; Lefebvre, Etienne

2008-01-01

We propose a simple method to extract the community structure of large networks. Our method is a heuristic method that is based on modularity optimization. It is shown to outperform all other known community detection methods in terms of computation time. Moreover, the quality of the communities detected is very good, as measured by the so-called modularity. This is shown first by identifying language communities in a Belgian mobile phone network of 2 million customers and by analysing a web graph of 118 million nodes and more than one billion links. The accuracy of our algorithm is also verified on ad hoc modular networks

Collective Learning in Games through Social Networks

NARCIS (Netherlands)

Kosterman, S.; Gierasimczuk, N.; Armentano, M.G.; Monteserin, A.; Tang, J.; Yannibelli, V.

2015-01-01

This paper argues that combining social networks communication and games can positively influence the learning behavior of players. We propose a computational model that combines features of social network learning (communication) and game-based learning (strategy reinforcement). The focus is on

Effects of multi-state links in network community detection

International Nuclear Information System (INIS)

Rocco, Claudio M.; Moronta, José; Ramirez-Marquez, José E.; Barker, Kash

2017-01-01

A community is defined as a group of nodes of a network that are densely interconnected with each other but only sparsely connected with the rest of the network. The set of communities (i.e., the network partition) and their inter-community links could be derived using special algorithms account for the topology of the network and, in certain cases, the possible weights associated to the links. In general, the set of weights represents some characteristic as capacity, flow and reliability, among others. The effects of considering weights could be translated to obtain a different partition. In many real situations, particularly when modeling infrastructure systems, networks must be modeled as multi-state networks (e.g., electric power networks). In such networks, each link is characterized by a vector of known random capacities (i.e., the weight on each link could vary according to a known probability distribution). In this paper a simple Monte Carlo approach is proposed to evaluate the effects of multi-state links on community detection as well as on the performance of the network. The approach is illustrated with the topology of an electric power system. - Highlights: • Identify network communities when considering multi-state links. • Identified how effects of considering weights translate to different partition. • Identified importance of Inter-Community Links and changes with respect to community. • Preamble to performing a resilience assessment able to mimic the evolution of the state of each community.

Learning and Best Practices for Learning in Open-Source Software Communities

Science.gov (United States)

Singh, Vandana; Holt, Lila

2013-01-01

This research is about participants who use open-source software (OSS) discussion forums for learning. Learning in online communities of education as well as non-education-related online communities has been studied under the lens of social learning theory and situated learning for a long time. In this research, we draw parallels among these two…

THE SCHOOL AS A LEARNING COMMUNITY

Directory of Open Access Journals (Sweden)

Cintya Arely Hernández-López

2015-07-01

Full Text Available In the present study is to weight the learning communities, starting to know the approach that has a school in the Chihuahua state to become a learning community, expecting describe how the school gathers the elements to operate as such. The method that was in use was the study of case, resting on the technologies of observation, interview and survey, same that complemented each other with the information that came from the survey and from the analysis of the “portafolio”. The case of study though it presents characteristics that demonstrate inside a community of learning as quality, collaborative work however the institution does not possess the opening and the participation of the involved ones, being an obstacle for the consolidation and benefit of the educational community; ith what there meets distant the possibility that this politics to turn to the school in a community of learning could be consolidate.

Overlapping community detection in networks with positive and negative links

International Nuclear Information System (INIS)

Chen, Y; Wang, X L; Yuan, B; Tang, B Z

2014-01-01

Complex networks considering both positive and negative links have gained considerable attention during the past several years. Community detection is one of the main challenges for complex network analysis. Most of the existing algorithms for community detection in a signed network aim at providing a hard-partition of the network where any node should belong to a community or not. However, they cannot detect overlapping communities where a node is allowed to belong to multiple communities. The overlapping communities widely exist in many real-world networks. In this paper, we propose a signed probabilistic mixture (SPM) model for overlapping community detection in signed networks. Compared with the existing models, the advantages of our methodology are (i) providing soft-partition solutions for signed networks; (ii) providing soft memberships of nodes. Experiments on a number of signed networks show that our SPM model: (i) can identify assortative structures or disassortative structures as the same as other state-of-the-art models; (ii) can detect overlapping communities; (iii) outperforms other state-of-the-art models at shedding light on the community detection in synthetic signed networks. (paper)

Supervised Learning with Complex-valued Neural Networks

CERN Document Server

Suresh, Sundaram; Savitha, Ramasamy

2013-01-01

Recent advancements in the field of telecommunications, medical imaging and signal processing deal with signals that are inherently time varying, nonlinear and complex-valued. The time varying, nonlinear characteristics of these signals can be effectively analyzed using artificial neural networks.  Furthermore, to efficiently preserve the physical characteristics of these complex-valued signals, it is important to develop complex-valued neural networks and derive their learning algorithms to represent these signals at every step of the learning process. This monograph comprises a collection of new supervised learning algorithms along with novel architectures for complex-valued neural networks. The concepts of meta-cognition equipped with a self-regulated learning have been known to be the best human learning strategy. In this monograph, the principles of meta-cognition have been introduced for complex-valued neural networks in both the batch and sequential learning modes. For applications where the computati...

Changing Conditions for Networked Learning?

DEFF Research Database (Denmark)

Ryberg, Thomas

2011-01-01

in describing the novel pedagogical potentials of these new technologies and practices (e.g. in debates around virtual learning environments versus personal learning environment). Likewise, I shall briefly discuss the notions of ‘digital natives’ or ‘the net generation’ from a critical perspective...... of social technologies. I argue that we are seeing the emergence of new architectures and scales of participation, collaboration and networking e.g. through interesting formations of learning networks at different levels of scale, for different purposes and often bridging boundaries such as formal...

Learning-parameter adjustment in neural networks

Science.gov (United States)

Heskes, Tom M.; Kappen, Bert

1992-06-01

We present a learning-parameter adjustment algorithm, valid for a large class of learning rules in neural-network literature. The algorithm follows directly from a consideration of the statistics of the weights in the network. The characteristic behavior of the algorithm is calculated, both in a fixed and a changing environment. A simple example, Widrow-Hoff learning for statistical classification, serves as an illustration.

Researching Design, Experience and Practice of Networked Learning

DEFF Research Database (Denmark)

Hodgson, Vivien; de Laat, Maarten; McConnell, David

2014-01-01

and final section draws attention to a growing topic of interest within networked learning: that of networked learning in informal practices. In addition, we provide a reflection on the theories, methods and settings featured in the networked learning research of the chapters. We conclude the introduction...

Learning Bayesian networks for discrete data

KAUST Repository

Liang, Faming

2009-02-01

Bayesian networks have received much attention in the recent literature. In this article, we propose an approach to learn Bayesian networks using the stochastic approximation Monte Carlo (SAMC) algorithm. Our approach has two nice features. Firstly, it possesses the self-adjusting mechanism and thus avoids essentially the local-trap problem suffered by conventional MCMC simulation-based approaches in learning Bayesian networks. Secondly, it falls into the class of dynamic importance sampling algorithms; the network features can be inferred by dynamically weighted averaging the samples generated in the learning process, and the resulting estimates can have much lower variation than the single model-based estimates. The numerical results indicate that our approach can mix much faster over the space of Bayesian networks than the conventional MCMC simulation-based approaches. © 2008 Elsevier B.V. All rights reserved.

A new hierarchical method to find community structure in networks

Science.gov (United States)

Saoud, Bilal; Moussaoui, Abdelouahab

2018-04-01

Community structure is very important to understand a network which represents a context. Many community detection methods have been proposed like hierarchical methods. In our study, we propose a new hierarchical method for community detection in networks based on genetic algorithm. In this method we use genetic algorithm to split a network into two networks which maximize the modularity. Each new network represents a cluster (community). Then we repeat the splitting process until we get one node at each cluster. We use the modularity function to measure the strength of the community structure found by our method, which gives us an objective metric for choosing the number of communities into which a network should be divided. We demonstrate that our method are highly effective at discovering community structure in both computer-generated and real-world network data.

Exploratory community sensing in social networks

Science.gov (United States)

Khrabrov, Alexy; Stocco, Gabriel; Cybenko, George

2010-04-01

Social networks generally provide an implementation of some kind of groups or communities which users can voluntarily join. Twitter does not have this functionality, and there is no notion of a formal group or community. We propose a method for identification of communities and assignment of semantic meaning to the discussion topics of the resulting communities. Using this analysis method and a sample of roughly a month's worth of Tweets from Twitter's "gardenhose" feed, we demonstrate the discovery of meaningful user communities on Twitter. We examine Twitter data streaming in real time and treat it as a sensor. Twitter is a social network which pioneered microblogging with the messages fitting an SMS, and a variety of clients, browsers, smart phones and PDAs are used for status updates by individuals, businesses, media outlets and even devices all over the world. Often an aggregate trend of such statuses may represent an important development in the world, which has been demonstrated with the Iran and Moldova elections and the anniversary of the Tiananmen in China. We propose using Twitter as a sensor, tracking individuals and communities of interest, and characterizing individual roles and dynamics of their communications. We developed a novel algorithm of community identification in social networks based on direct communication, as opposed to linking. We show ways to find communities of interest and then browse their neighborhoods by either similarity or diversity of individuals and groups adjacent to the one of interest. We use frequent collocations and statistically improbable phrases to summarize the focus of the community, giving a quick overview of its main topics. Our methods provide insight into the largest social sensor network in the world and constitute a platform for social sensing.

Research on Community Structure in Bus Transport Networks

International Nuclear Information System (INIS)

Yang Xuhua; Wang Bo; Sun Youxian

2009-01-01

We abstract the bus transport networks (BTNs) to two kinds of complex networks with space L and space P methods respectively. Using improved community detecting algorithm (PKM agglomerative algorithm), we analyze the community property of two kinds of BTNs graphs. The results show that the BTNs graph described with space L method have obvious community property, but the other kind of BTNs graph described with space P method have not. The reason is that the BTNs graph described with space P method have the intense overlapping community property and general community division algorithms can not identify this kind of community structure. To overcome this problem, we propose a novel community structure called N-depth community and present a corresponding community detecting algorithm, which can detect overlapping community. Applying the novel community structure and detecting algorithm to a BTN evolution model described with space P, whose network property agrees well with real BTNs', we get obvious community property. (general)

Community structures and role detection in music networks

Science.gov (United States)

Teitelbaum, T.; Balenzuela, P.; Cano, P.; Buldú, Javier M.

2008-12-01

We analyze the existence of community structures in two different social networks using data obtained from similarity and collaborative features between musical artists. Our analysis reveals some characteristic organizational patterns and provides information about the driving forces behind the growth of the networks. In the similarity network, we find a strong correlation between clusters of artists and musical genres. On the other hand, the collaboration network shows two different kinds of communities: rather small structures related to music bands and geographic zones, and much bigger communities built upon collaborative clusters with a high number of participants related through the period the artists were active. Finally, we detect the leading artists inside their corresponding communities and analyze their roles in the network by looking at a few topological properties of the nodes.

Learning network theory : its contribution to our understanding of work-based learning projects and learning climate

NARCIS (Netherlands)

Poell, R.F.; Moorsel, M.A.A.H. van

1996-01-01

This paper discusses the relevance of Van der Krogt's learning network theory (1995) for our understanding of the concepts of work-related learning projects and learning climate in organisations. The main assumptions of the learning network theory are presented and transferred to the level of

Identification of hybrid node and link communities in complex networks.

Science.gov (United States)

He, Dongxiao; Jin, Di; Chen, Zheng; Zhang, Weixiong

2015-03-02

Identifying communities in complex networks is an effective means for analyzing complex systems, with applications in diverse areas such as social science, engineering, biology and medicine. Finding communities of nodes and finding communities of links are two popular schemes for network analysis. These schemes, however, have inherent drawbacks and are inadequate to capture complex organizational structures in real networks. We introduce a new scheme and an effective approach for identifying complex mixture structures of node and link communities, called hybrid node-link communities. A central piece of our approach is a probabilistic model that accommodates node, link and hybrid node-link communities. Our extensive experiments on various real-world networks, including a large protein-protein interaction network and a large network of semantically associated words, illustrated that the scheme for hybrid communities is superior in revealing network characteristics. Moreover, the new approach outperformed the existing methods for finding node or link communities separately.

Identification of hybrid node and link communities in complex networks

Science.gov (United States)

He, Dongxiao; Jin, Di; Chen, Zheng; Zhang, Weixiong

2015-03-01

Identifying communities in complex networks is an effective means for analyzing complex systems, with applications in diverse areas such as social science, engineering, biology and medicine. Finding communities of nodes and finding communities of links are two popular schemes for network analysis. These schemes, however, have inherent drawbacks and are inadequate to capture complex organizational structures in real networks. We introduce a new scheme and an effective approach for identifying complex mixture structures of node and link communities, called hybrid node-link communities. A central piece of our approach is a probabilistic model that accommodates node, link and hybrid node-link communities. Our extensive experiments on various real-world networks, including a large protein-protein interaction network and a large network of semantically associated words, illustrated that the scheme for hybrid communities is superior in revealing network characteristics. Moreover, the new approach outperformed the existing methods for finding node or link communities separately.

Cooperative Learning for Distributed In-Network Traffic Classification

Science.gov (United States)

Joseph, S. B.; Loo, H. R.; Ismail, I.; Andromeda, T.; Marsono, M. N.

2017-04-01

Inspired by the concept of autonomic distributed/decentralized network management schemes, we consider the issue of information exchange among distributed network nodes to network performance and promote scalability for in-network monitoring. In this paper, we propose a cooperative learning algorithm for propagation and synchronization of network information among autonomic distributed network nodes for online traffic classification. The results show that network nodes with sharing capability perform better with a higher average accuracy of 89.21% (sharing data) and 88.37% (sharing clusters) compared to 88.06% for nodes without cooperative learning capability. The overall performance indicates that cooperative learning is promising for distributed in-network traffic classification.

Joint community and anomaly tracking in dynamic networks

OpenAIRE

Baingana, Brian; Giannakis, Georgios B.

2015-01-01

Most real-world networks exhibit community structure, a phenomenon characterized by existence of node clusters whose intra-edge connectivity is stronger than edge connectivities between nodes belonging to different clusters. In addition to facilitating a better understanding of network behavior, community detection finds many practical applications in diverse settings. Communities in online social networks are indicative of shared functional roles, or affiliation to a common socio-economic st...

Information dynamics algorithm for detecting communities in networks

Science.gov (United States)

Massaro, Emanuele; Bagnoli, Franco; Guazzini, Andrea; Lió, Pietro

2012-11-01

The problem of community detection is relevant in many scientific disciplines, from social science to statistical physics. Given the impact of community detection in many areas, such as psychology and social sciences, we have addressed the issue of modifying existing well performing algorithms by incorporating elements of the domain application fields, i.e. domain-inspired. We have focused on a psychology and social network-inspired approach which may be useful for further strengthening the link between social network studies and mathematics of community detection. Here we introduce a community-detection algorithm derived from the van Dongen's Markov Cluster algorithm (MCL) method [4] by considering networks' nodes as agents capable to take decisions. In this framework we have introduced a memory factor to mimic a typical human behavior such as the oblivion effect. The method is based on information diffusion and it includes a non-linear processing phase. We test our method on two classical community benchmark and on computer generated networks with known community structure. Our approach has three important features: the capacity of detecting overlapping communities, the capability of identifying communities from an individual point of view and the fine tuning the community detectability with respect to prior knowledge of the data. Finally we discuss how to use a Shannon entropy measure for parameter estimation in complex networks.

Distance learning, problem based learning and dynamic knowledge networks.

Science.gov (United States)

Giani, U; Martone, P

1998-06-01

This paper is an attempt to develop a distance learning model grounded upon a strict integration of problem based learning (PBL), dynamic knowledge networks (DKN) and web tools, such as hypermedia documents, synchronous and asynchronous communication facilities, etc. The main objective is to develop a theory of distance learning based upon the idea that learning is a highly dynamic cognitive process aimed at connecting different concepts in a network of mutually supporting concepts. Moreover, this process is supposed to be the result of a social interaction that has to be facilitated by the web. The model was tested by creating a virtual classroom of medical and nursing students and activating a learning session on the concept of knowledge representation in health sciences.

CoCoRaHS (The Community Collaborative Rain, Hail and Snow Network): Analysis of Participant Survey Data to Uncover Learning through Participation

Science.gov (United States)

Holzer, M. A.; Zimmerman, T.; Doesken, N. J.; Reges, H. W.; Newman, N.; Turner, J.; Schwalbe, Z.

2010-12-01

CoCoRaHS (The Community Collaborative Rain, Hail and Snow network) is based out of Fort Collins Colorado and is an extremely successful citizen science project with over 15,000 volunteers collecting valuable precipitation data. Forecasters and scientists use data from this dense network to illuminate and illustrate the high small-scale variability of precipitation across the nation. This presentation will discuss the results of a survey of CoCoRaHS participants as related to 1) citizen scientists’ motivation and learning; 2) the challenges of identifying how people learn science in citizen science projects; and 3) a potential research-based framework for how people learn through engaging in the data collection within in a citizen science project. A comprehensive survey of 14,500 CoCoRaHS observers was recently conducted to uncover participant perceptions of numerous aspects of the CoCoRaHS program, including its goal of increasing climate literacy. The survey yielded a response rate of over 50%, and included measures of motivation, engagement and learning. In relationship to motivation and learning, the survey revealed that most (57.1%) observers would make precipitation observations regardless of being a CoCoRaHS volunteer, therefore their motivation is related to their inherent level of interest in weather. Others are motivated by their desire to learn more about weather and climate, they want to contribute to a scientific project, they think its fun, and/or it provides a sense of community. Because so many respondents already had knowledge and interest in weather and climate, identifying how and what people learn through participating was a challenge. However, the narrow project focus of collecting and reporting of local precipitation assisted in identifying aspects of learning. For instance, most (46.4%) observers said they increased their knowledge about the local variability in precipitation even though they had been collecting precipitation data for many

Organisational aspects and benchmarking of e-learning initiatives: a case study with South African community health workers.

Science.gov (United States)

Reisach, Ulrike; Weilemann, Mitja

2016-06-01

South Africa desperately needs a comprehensive approach to fight HIV/AIDS. Education is crucial to reach this goal and Internet and e-learning could offer huge opportunities to broaden and deepen the knowledge basis. But due to the huge societal and digital divide between rich and poor areas, e-learning is difficult to realize in the townships. Community health workers often act as mediators and coaches for people seeking medical and personal help. They could give good advice regarding hygiene, nutrition, protection of family members in case of HIV/AIDS and finding legal ways to earn one's living if they were trained to do so. Therefore they need to have a broader general knowledge. Since learning opportunities in the townships are scarce, a system for e-learning has to be created in order to overcome the lack of experience with computers or the Internet and to enable them to implement a network of expertise. The article describes how the best international resources on basic medical knowledge, HIV/AIDS as well as on basic economic and entrepreneurial skills were benchmarked to be integrated into an e-learning system. After tests with community health workers, researchers developed recommendations on building a self-sustaining system for learning, including a network of expertise and best practice sharing. The article explains the opportunities and challenges for community health workers, which could provide information for other parts of the world with similar preconditions of rural poverty. © The Author(s) 2015.

Epidemic spreading in time-varying community networks.

Science.gov (United States)

Ren, Guangming; Wang, Xingyuan

2014-06-01

The spreading processes of many infectious diseases have comparable time scale as the network evolution. Here, we present a simple networks model with time-varying community structure, and investigate susceptible-infected-susceptible epidemic spreading processes in this model. By both theoretic analysis and numerical simulations, we show that the efficiency of epidemic spreading in this model depends intensively on the mobility rate q of the individuals among communities. We also find that there exists a mobility rate threshold qc. The epidemic will survive when q > qc and die when q epidemic spreading in complex networks with community structure.

Topic-oriented community detection of rating-based social networks

Directory of Open Access Journals (Sweden)

Ali Reihanian

2016-07-01

Full Text Available Nowadays, real world social networks contain a vast range of information including shared objects, comments, following information, etc. Finding meaningful communities in this kind of networks is an interesting research area and has attracted the attention of many researchers. The community structure of complex networks reveals both their organization and hidden relations among their constituents. Most of the researches in the field of community detection mainly focus on the topological structure of the network without performing any content analysis. In recent years, a number of researches have proposed approaches which consider both the contents that are interchanged in networks, and the topological structures of the networks in order to find more meaningful communities. In this research, the effect of topic analysis in finding more meaningful communities in social networking sites in which the users express their feelings toward different objects (like movies by means of rating is demonstrated by performing extensive experiments.

Contingent factors affecting network learning

OpenAIRE

Peters, Linda D.; Pressey, Andrew D.; Johnston, Wesley J.

2016-01-01

To increase understanding of the impact of individuals on organizational learning processes, this paper explores the impact of individual cognition and action on the absorptive capacity process of the wider network. In particular this study shows how contingent factors such as social integration mechanisms and power relationships influence how network members engage in, and benefit from, learning. The use of cognitive consistency and sensemaking theory enables examination of how these conting...

Emergence of communities and diversity in social networks

OpenAIRE

Han, Xiao; Cao, Shinan; Shen, Zhesi; Zhang, Boyu; Wang, Wen-Xu; Cressman, Ross; Stanley, H. Eugene

2017-01-01

Understanding how communities emerge is a fundamental problem in social and economic systems. Here, we experimentally explore the emergence of communities in social networks, using the ultimatum game as a paradigm for capturing individual interactions. We find the emergence of diverse communities in static networks is the result of the local interaction between responders with inherent heterogeneity and rational proposers in which the former act as community leaders. In contrast, communities ...

Gender differences in collaborative learning over online social networks: Epistemological beliefs and behaviors

Directory of Open Access Journals (Sweden)

Rosanna Y.-Y. Chan

2013-09-01

Full Text Available Online social networks are popular venues for computer-supported collaborative work and computer-supported collaborative learning. Professionals within the same discipline, such as software developers, often interact over various social network sites for knowledge updates and collective understandings. The current study aims at gathering empirical evidences concerning gender differences in online social network beliefs and behaviors. A total of 53 engineering postgraduate students were engaged in a blogging community for collaborative learning. Participants’ beliefs about collaboration and nature of knowledge and knowing (i.e. epistemological beliefs are investigated. More specifically, social network analysis metrics including in-degree, out-degree, closeness centrality, and betweenness centrality are obtained from an 8-interval longitudinal SNA. Methodologically speaking, the current work puts forward mixed methods of longitudinal SNA and quantitative beliefs survey to explore online social network participants’ beliefs and behaviors. The study’s findings demonstrate significant gender differences in collaborative learning through online social networks, including (1 female engineering postgraduate students engage significantly more actively in online communications, (2 male engineering postgraduate students are more likely to be the potential controllers of information flows, and (3 gender differences exist in belief gains related to social aspects, but not individual's epistemic aspects. Overall, participants in both genders demonstrated enhanced beliefs in collaboration as well as the nature of knowledge and knowing.

Interaction Networks: Generating High Level Hints Based on Network Community Clustering

Science.gov (United States)

Eagle, Michael; Johnson, Matthew; Barnes, Tiffany

2012-01-01

We introduce a novel data structure, the Interaction Network, for representing interaction-data from open problem solving environment tutors. We show how using network community detecting techniques are used to identify sub-goals in problems in a logic tutor. We then use those community structures to generate high level hints between sub-goals.…

Super-Resolution Community Detection for Layer-Aggregated Multilayer Networks

Directory of Open Access Journals (Sweden)

Dane Taylor

2017-09-01

Full Text Available Applied network science often involves preprocessing network data before applying a network-analysis method, and there is typically a theoretical disconnect between these steps. For example, it is common to aggregate time-varying network data into windows prior to analysis, and the trade-offs of this preprocessing are not well understood. Focusing on the problem of detecting small communities in multilayer networks, we study the effects of layer aggregation by developing random-matrix theory for modularity matrices associated with layer-aggregated networks with N nodes and L layers, which are drawn from an ensemble of Erdős–Rényi networks with communities planted in subsets of layers. We study phase transitions in which eigenvectors localize onto communities (allowing their detection and which occur for a given community provided its size surpasses a detectability limit K^{*}. When layers are aggregated via a summation, we obtain K^{*}∝O(sqrt[NL]/T, where T is the number of layers across which the community persists. Interestingly, if T is allowed to vary with L, then summation-based layer aggregation enhances small-community detection even if the community persists across a vanishing fraction of layers, provided that T/L decays more slowly than O(L^{-1/2}. Moreover, we find that thresholding the summation can, in some cases, cause K^{*} to decay exponentially, decreasing by orders of magnitude in a phenomenon we call super-resolution community detection. In other words, layer aggregation with thresholding is a nonlinear data filter enabling detection of communities that are otherwise too small to detect. Importantly, different thresholds generally enhance the detectability of communities having different properties, illustrating that community detection can be obscured if one analyzes network data using a single threshold.

Super-Resolution Community Detection for Layer-Aggregated Multilayer Networks.

Science.gov (United States)

Taylor, Dane; Caceres, Rajmonda S; Mucha, Peter J

2017-01-01

Applied network science often involves preprocessing network data before applying a network-analysis method, and there is typically a theoretical disconnect between these steps. For example, it is common to aggregate time-varying network data into windows prior to analysis, and the trade-offs of this preprocessing are not well understood. Focusing on the problem of detecting small communities in multilayer networks, we study the effects of layer aggregation by developing random-matrix theory for modularity matrices associated with layer-aggregated networks with N nodes and L layers, which are drawn from an ensemble of Erdős-Rényi networks with communities planted in subsets of layers. We study phase transitions in which eigenvectors localize onto communities (allowing their detection) and which occur for a given community provided its size surpasses a detectability limit K * . When layers are aggregated via a summation, we obtain [Formula: see text], where T is the number of layers across which the community persists. Interestingly, if T is allowed to vary with L , then summation-based layer aggregation enhances small-community detection even if the community persists across a vanishing fraction of layers, provided that T/L decays more slowly than ( L -1/2 ). Moreover, we find that thresholding the summation can, in some cases, cause K * to decay exponentially, decreasing by orders of magnitude in a phenomenon we call super-resolution community detection. In other words, layer aggregation with thresholding is a nonlinear data filter enabling detection of communities that are otherwise too small to detect. Importantly, different thresholds generally enhance the detectability of communities having different properties, illustrating that community detection can be obscured if one analyzes network data using a single threshold.

Information transfer in community structured multiplex networks

Science.gov (United States)

Solé Ribalta, Albert; Granell, Clara; Gómez, Sergio; Arenas, Alex

2015-08-01

The study of complex networks that account for different types of interactions has become a subject of interest in the last few years, specially because its representational power in the description of users interactions in diverse online social platforms (Facebook, Twitter, Instagram, etc.). The mathematical description of these interacting networks has been coined under the name of multilayer networks, where each layer accounts for a type of interaction. It has been shown that diffusive processes on top of these networks present a phenomenology that cannot be explained by the naive superposition of single layer diffusive phenomena but require the whole structure of interconnected layers. Nevertheless, the description of diffusive phenomena on multilayer networks has obviated the fact that social networks have strong mesoscopic structure represented by different communities of individuals driven by common interests, or any other social aspect. In this work, we study the transfer of information in multilayer networks with community structure. The final goal is to understand and quantify, if the existence of well-defined community structure at the level of individual layers, together with the multilayer structure of the whole network, enhances or deteriorates the diffusion of packets of information.

Information transfer in community structured multiplex networks

Directory of Open Access Journals (Sweden)

Albert eSolé Ribalta

2015-08-01

Full Text Available The study of complex networks that account for different types of interactions has become a subject of interest in the last few years, specially because its representational power in the description of users interactions in diverse online social platforms (Facebook, Twitter, Instagram, etc.. The mathematical description of these interacting networks has been coined under the name of multilayer networks, where each layer accounts for a type of interaction. It has been shown that diffusive processes on top of these networks present a phenomenology that cannot be explained by the naive superposition of single layer diffusive phenomena but require the whole structure of interconnected layers. Nevertheless, the description of diffusive phenomena on multilayer networks has obviated the fact that social networks have strong mesoscopic structure represented by different communities of individuals driven by common interests, or any other social aspect. In this work, we study the transfer of information in multilayer networks with community structure. The final goal is to understand and quantify, if the existence of well-defined community structure at the level of individual layers, together with the multilayer structure of the whole network, enhances or deteriorates the diffusion of packets of information.

Learning network theory : its contribution to our understanding of work-based learning projects and learning climate

OpenAIRE

Poell, R.F.; Moorsel, M.A.A.H. van

1996-01-01

This paper discusses the relevance of Van der Krogt's learning network theory (1995) for our understanding of the concepts of work-related learning projects and learning climate in organisations. The main assumptions of the learning network theory are presented and transferred to the level of learning groups in organisations. Four theoretical types of learning projects are distinguished. Four different approaches to the learning climate of work groups are compared to the approach offered by t...

CosmoQuest Collaborative: Galvanizing a Dynamic Professional Learning Network

Science.gov (United States)

Cobb, Whitney; Bracey, Georgia; Buxner, Sanlyn; Gay, Pamela L.; Noel-Storr, Jacob; CosmoQuest Team

2016-10-01

The CosmoQuest Collaboration offers in-depth experiences to diverse audiences around the nation and the world through pioneering citizen science in a virtual research facility. An endeavor between universities, research institutes, and NASA centers, CosmoQuest brings together scientists, educators, researchers, programmers—and citizens of all ages—to explore and make sense of our solar system and beyond. Leveraging human networks to expand NASA science, scaffolded by an educational framework that inspires lifelong learners, CosmoQuest engages citizens in analyzing and interpreting real NASA data, inspiring questions and defining problems.The QuestionLinda Darling-Hammond calls for professional development to be: "focused on the learning and teaching of specific curriculum content [i.e. NGSS disciplinary core ideas]; organized around real problems of practice [i.e. NGSS science and engineering practices] … [and] connected to teachers' collaborative work in professional learning community...." (2012) In light of that, what is the unique role CosmoQuest's virtual research facility can offer NASA STEM education?A Few AnswersThe CosmoQuest Collaboration actively engages scientists in education, and educators (and learners) in science. CosmoQuest uses social channels to empower and expand NASA's learning community through a variety of media, including science and education-focused hangouts, virtual star parties, and social media. In addition to creating its own supportive, standards-aligned materials, CosmoQuest offers a hub for excellent resources and materials throughout NASA and the larger astronomy community.In support of CosmoQuest citizen science opportunities, CQ initiatives (Learning Space, S-ROSES, IDEASS, Educator Zone) will be leveraged and shared through the CQPLN. CosmoQuest can be present and alive in the awareness its growing learning community.Finally, to make the CosmoQuest PLN truly relevant, it aims to encourage partnerships between scientists

Learning and structure of neuronal networks

Indian Academy of Sciences (India)

We study the effect of learning dynamics on network topology. Firstly, a network of discrete dynamical systems is considered for this purpose and the coupling strengths are made to evolve according to a temporal learning rule that is based on the paradigm of spike-time-dependent plasticity (STDP). This incorporates ...

Stochastic Variational Learning in Recurrent Spiking Networks

Directory of Open Access Journals (Sweden)

Danilo eJimenez Rezende

2014-04-01

Full Text Available The ability to learn and perform statistical inference with biologically plausible recurrent network of spiking neurons is an important step towards understanding perception and reasoning. Here we derive and investigate a new learning rule for recurrent spiking networks with hidden neurons, combining principles from variational learning and reinforcement learning. Our network defines a generative model over spike train histories and the derived learning rule has the form of a local Spike Timing Dependent Plasticity rule modulated by global factors (neuromodulators conveying information about ``novelty on a statistically rigorous ground.Simulations show that our model is able to learn bothstationary and non-stationary patterns of spike trains.We also propose one experiment that could potentially be performed with animals in order to test the dynamics of the predicted novelty signal.

Stochastic variational learning in recurrent spiking networks.

Science.gov (United States)

Jimenez Rezende, Danilo; Gerstner, Wulfram

2014-01-01

The ability to learn and perform statistical inference with biologically plausible recurrent networks of spiking neurons is an important step toward understanding perception and reasoning. Here we derive and investigate a new learning rule for recurrent spiking networks with hidden neurons, combining principles from variational learning and reinforcement learning. Our network defines a generative model over spike train histories and the derived learning rule has the form of a local Spike Timing Dependent Plasticity rule modulated by global factors (neuromodulators) conveying information about "novelty" on a statistically rigorous ground. Simulations show that our model is able to learn both stationary and non-stationary patterns of spike trains. We also propose one experiment that could potentially be performed with animals in order to test the dynamics of the predicted novelty signal.

Learning Reproducibility with a Yearly Networking Contest

KAUST Repository

Canini, Marco

2017-08-10

Better reproducibility of networking research results is currently a major goal that the academic community is striving towards. This position paper makes the case that improving the extent and pervasiveness of reproducible research can be greatly fostered by organizing a yearly international contest. We argue that holding a contest undertaken by a plurality of students will have benefits that are two-fold. First, it will promote hands-on learning of skills that are helpful in producing artifacts at the replicable-research level. Second, it will advance the best practices regarding environments, testbeds, and tools that will aid the tasks of reproducibility evaluation committees by and large.

Analyzing Learning in Professional Learning Communities: A Conceptual Framework

Science.gov (United States)

Van Lare, Michelle D.; Brazer, S. David

2013-01-01

The purpose of this article is to build a conceptual framework that informs current understanding of how professional learning communities (PLCs) function in conjunction with organizational learning. The combination of sociocultural learning theories and organizational learning theories presents a more complete picture of PLC processes that has…

Community Detection for Multiplex Social Networks Based on Relational Bayesian Networks

DEFF Research Database (Denmark)

Jiang, Jiuchuan; Jaeger, Manfred

2014-01-01

Many techniques have been proposed for community detection in social networks. Most of these techniques are only designed for networks defined by a single relation. However, many real networks are multiplex networks that contain multiple types of relations and different attributes on the nodes...

Building Learning Communities: Foundations for Good Practice

Science.gov (United States)

Davies, Alison; Ramsay, Jill; Lindfield, Helen; Couperthwaite, John

2005-01-01

The School of Health Sciences at the University of Birmingham provided opportunities for the development of student learning communities and online resources within the neurological module of the BSc Physiotherapy degree programme. These learning communities were designed to facilitate peer and independent learning in core aspects underpinning…

Extracting weights from edge directions to find communities in directed networks

International Nuclear Information System (INIS)

Lai, Darong; Lu, Hongtao; Nardini, Christine

2010-01-01

Community structures are found to exist ubiquitously in real-world complex networks. We address here the problem of community detection in directed networks. Most of the previous literature ignores edge directions and applies methods designed for community detection in undirected networks, which discards valuable information and often fails when different communities are defined on the basis of incoming and outgoing edges. We suggest extracting information about edge directions using a PageRank random walk and translating such information into edge weights. After extraction we obtain a new weighted directed network in which edge directions can then be safely ignored. We thus transform community detection in directed networks into community detection in reweighted undirected networks. Such an approach can benefit directly from the large volume of algorithms for the detection of communities in undirected networks already developed, since it is not obvious how to extend these algorithms to account for directed networks and the procedure is often difficult. Validations on synthetic and real-world networks demonstrate that the proposed framework can effectively detect communities in directed networks

Program Spotlight: National Outreach Network's Community Health Educators

Science.gov (United States)

National Outreach Network of Community Health Educators located at Community Network Program Centers, Partnerships to Advance Cancer Health Equity, and NCI-designated cancer centers help patients and their families receive survivorship support.

Consumer engagement in social networks brand community

OpenAIRE

Rybakovas, Paulius

2016-01-01

Consumers increasingly integrate social media into their day-to-day lives. For companies consumer engagement in a brand community on social network is becoming increasingly important for developing relations with consumers. Consumer engagement in a brand community on social network creates a dynamic relationship between the community members and the brand which contributes to an increase in consumer loyalty to the brand. The literature is abundant of studies, which examines the consumer engag...

Networked Identities

DEFF Research Database (Denmark)

Ryberg, Thomas; Larsen, Malene Charlotte

2008-01-01

of CoPs we shall argue that the metaphor or theory of networked learning is itself confronted with some central tensions and challenges that need to be addressed. We then explore these theoretical and analytic challenges to the network metaphor, through an analysis of a Danish social networking site. We......In this article we take up a critique of the concept of Communities of Practice (CoP) voiced by several authors, who suggest that networks may provide a better metaphor to understand social forms of organisation and learning. Through a discussion of the notion of networked learning and the critique...... argue that understanding meaning-making and ‘networked identities’ may be relevant analytic entry points in navigating the challenges....

Twenty-First Century Learning: Communities, Interaction and Ubiquitous Computing

Science.gov (United States)

Leh, Amy S.C.; Kouba, Barbara; Davis, Dirk

2005-01-01

Advanced technology makes 21st century learning, communities and interactions unique and leads people to an era of ubiquitous computing. The purpose of this article is to contribute to the discussion of learning in the 21st century. The paper will review literature on learning community, community learning, interaction, 21st century learning and…

THE IMPACTS OF SOCIAL NETWORKING SITES IN HIGHER LEARNING

Directory of Open Access Journals (Sweden)

Mohd Ishak Bin Ismail

2016-02-01

Full Text Available Social networking sites, a web-based application have permeated the boundary between personal lives and student lives. Nowadays, students in higher learning used social networking site such as Facebook to facilitate their learning through the academic collaboration which it further enhances students’ social capital. Social networking site has many advantages to improve students’ learning. To date, Facebook is the leading social networking sites at this time which it being widely used by students in higher learning to communicate to each other, to carry out academic collaboration and sharing resources. Learning through social networking sites is based on the social interaction which learning are emphasizing on students, real world resources, active students` participation, diversity of learning resources and the use of digital tools to deliver meaningful learning. Many studies found the positive, neutral and negative impact of social networking sites on academic performance. Thus, this study will determine the relationship between Facebook usage and academic achievement. Also, it will investigate the association of social capital and academic collaboration to Facebook usage.

Uncovering the community structure associated with the diffusion dynamics on networks

International Nuclear Information System (INIS)

Cheng, Xue-Qi; Shen, Hua-Wei

2010-01-01

As two main focuses of the study of complex networks, the community structure and the dynamics on networks have both attracted much attention in various scientific fields. However, it is still an open question how the community structure is associated with the dynamics on complex networks. In this paper, through investigating the diffusion process taking place on networks, we demonstrate that the intrinsic community structure of networks can be revealed by the stable local equilibrium states of the diffusion process. Furthermore, we show that such community structure can be directly identified through the optimization of the conductance of the network, which measures how easily the diffusion among different communities occurs. Tests on benchmark networks indicate that the conductance optimization method significantly outperforms the modularity optimization methods in identifying the community structure of networks. Applications to real world networks also demonstrate the effectiveness of the conductance optimization method. This work provides insights into the multiple topological scales of complex networks, and the community structure obtained can naturally reflect the diffusion capability of the underlying network

Sociospatial Knowledge Networks: Appraising Community as Place.

Science.gov (United States)

Skelly, Anne H.; Arcury, Thomas A.; Gesler, Wilbert M.; Cravey, Altha J.; Dougherty, Molly C.; Washburn, Sarah A.; Nash, Sally

2002-01-01

A new theory of geographical analysis--sociospatial knowledge networks--provides a framework for understanding the social and spatial locations of a community's health knowledge and beliefs. This theory is guiding an ethnographic study of health beliefs, knowledge, and knowledge networks in a diverse rural community at high risk for type-2…

Learning to Learn: A Hidden Dimension within Community Dance Practice

Science.gov (United States)

Barr, Sherrie

2013-01-01

This article explores ways of learning experienced by university dance students participating in a community dance project. The students were unfamiliar with community-based practices and found themselves needing to remediate held attitudes about dance. How the students came to approach their learning within the dance-making process drew on…

Incorporating profile information in community detection for online social networks

Science.gov (United States)

Fan, W.; Yeung, K. H.

2014-07-01

Community structure is an important feature in the study of complex networks. It is because nodes of the same community may have similar properties. In this paper we extend two popular community detection methods to partition online social networks. In our extended methods, the profile information of users is used for partitioning. We apply the extended methods in several sample networks of Facebook. Compared with the original methods, the community structures we obtain have higher modularity. Our results indicate that users' profile information is consistent with the community structure of their friendship network to some extent. To the best of our knowledge, this paper is the first to discuss how profile information can be used to improve community detection in online social networks.

The Use Of Social Networking Sites For Learning In Institutions Of Higher Learning

Directory of Open Access Journals (Sweden)

Mange Gladys Nkatha

2015-08-01

Full Text Available Abstract Institutions of higher learning are facing greater challenges to change and subjected to various transformations in the surrounding environment including technology. These challenge and motivate them to explore new ways to improve their teaching approaches. This study sought to investigate the use of social networking site in institutions of higher learning. To this end two objectives were formulated 1 to investigate the current state of the use of social networking sites by the students 2 investigate how social networking sites can be used to promote authentic learning in institutions of higher learning. The study adopted exploratory approach using descriptive survey design where a sample of 10 67 students were picked from Jomo Kenyatta University of Agriculture and Technology JKUAT main campus. The findings indicate the use of social networking sites is a viable option as the students are not only members of social networking sites but also that majority have access to the requisite technological devices. Additionally recommendations for ensuring authentic learning were presented. The researcher recommends the exploration of the leveraging of the existing social networking sites for learning in conjunction with key stakeholders.

Reflective learning in community-based dental education.

Science.gov (United States)

Deogade, Suryakant C; Naitam, Dinesh

2016-01-01

Community-based dental education (CBDE) is the implementation of dental education in a specific social context, which shifts a substantial part of dental clinical education from dental teaching institutional clinics to mainly public health settings. Dental students gain additional value from CBDE when they are guided through a reflective process of learning. We propose some key elements to the existing CBDE program that support meaningful personal learning experiences. Dental rotations of 'externships' in community-based clinical settings (CBCS) are year-long community-based placements and have proven to be strong learning environments where students develop good communication skills and better clinical reasoning and management skills. We look at the characteristics of CBDE and how the social and personal context provided in communities enhances dental education. Meaningfulness is created by the authentic context, which develops over a period of time. Structured reflection assignments and methods are suggested as key elements in the existing CBDE program. Strategies to enrich community-based learning experiences for dental students include: Photographic documentation; written narratives; critical incident reports; and mentored post-experiential small group discussions. A directed process of reflection is suggested as a way to increase the impact of the community learning experiences. We suggest key elements to the existing CBDE module so that the context-rich environment of CBDE allows for meaningful relations and experiences for dental students and enhanced learning.

Structure of Small World Innovation Network and Learning Performance

Directory of Open Access Journals (Sweden)

Shuang Song

2014-01-01

Full Text Available This paper examines the differences of learning performance of 5 MNCs (multinational corporations that filed the largest number of patents in China. We establish the innovation network with the patent coauthorship data by these 5 MNCs and classify the networks by the tail of distribution curve of connections. To make a comparison of the learning performance of these 5 MNCs with differing network structures, we develop an organization learning model by regarding the reality as having m dimensions, which denotes the heterogeneous knowledge about the reality. We further set n innovative individuals that are mutually interactive and own unique knowledge about the reality. A longer (shorter distance between the knowledge of the individual and the reality denotes a lower (higher knowledge level of that individual. Individuals interact with and learn from each other within the small-world network. By making 1,000 numerical simulations and averaging the simulated results, we find that the differing structure of the small-world network leads to the differences of learning performance between these 5 MNCs. The network monopolization negatively impacts and network connectivity positively impacts learning performance. Policy implications in the conclusion section suggest that to improve firm learning performance, it is necessary to establish a flat and connective network.

Epidemic spreading in time-varying community networks

Energy Technology Data Exchange (ETDEWEB)

Ren, Guangming, E-mail: wangxy@dlut.edu.cn, E-mail: ren-guang-ming@163.com [School of Electronic and Information, Guangdong Polytechnic Normal University, Guangzhou 510665 (China); Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian 116024 (China); Wang, Xingyuan, E-mail: wangxy@dlut.edu.cn, E-mail: ren-guang-ming@163.com [Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian 116024 (China)

2014-06-15

The spreading processes of many infectious diseases have comparable time scale as the network evolution. Here, we present a simple networks model with time-varying community structure, and investigate susceptible-infected-susceptible epidemic spreading processes in this model. By both theoretic analysis and numerical simulations, we show that the efficiency of epidemic spreading in this model depends intensively on the mobility rate q of the individuals among communities. We also find that there exists a mobility rate threshold q{sub c}. The epidemic will survive when q > q{sub c} and die when q < q{sub c}. These results can help understanding the impacts of human travel on the epidemic spreading in complex networks with community structure.

Epidemic spreading in time-varying community networks

International Nuclear Information System (INIS)

Ren, Guangming; Wang, Xingyuan

2014-01-01

The spreading processes of many infectious diseases have comparable time scale as the network evolution. Here, we present a simple networks model with time-varying community structure, and investigate susceptible-infected-susceptible epidemic spreading processes in this model. By both theoretic analysis and numerical simulations, we show that the efficiency of epidemic spreading in this model depends intensively on the mobility rate q of the individuals among communities. We also find that there exists a mobility rate threshold q c . The epidemic will survive when q > q c and die when q  c . These results can help understanding the impacts of human travel on the epidemic spreading in complex networks with community structure

Teaching & Learning for International Students in a 'Learning Community': Creating, Sharing and Building Knowledge

Directory of Open Access Journals (Sweden)

Linzi Kemp, PhD

2010-08-01

Full Text Available This article considers the culture of learning communities for effective teaching. A learning community is defined here as an environment where learners are brought together to share information, to learn from each other, and to create new knowledge. The individual student develops her/his own learning by building on learning from others. In a learning community approach to teaching, educators can ensure that students gain workplace skills such as collaboration, creativity, critical thinking, and problem solving. In this case study, it is shown how an active learning community, introduced into a blended teaching environment (face-to-face and virtual, effectively supported international undergraduates in the building of knowledge and workplace skills.

A cooperative game framework for detecting overlapping communities in social networks

Science.gov (United States)

Jonnalagadda, Annapurna; Kuppusamy, Lakshmanan

2018-02-01

Community detection in social networks is a challenging and complex task, which received much attention from researchers of multiple domains in recent years. The evolution of communities in social networks happens merely due to the self-interest of the nodes. The interesting feature of community structure in social networks is the multi membership of the nodes resulting in overlapping communities. Assuming the nodes of the social network as self-interested players, the dynamics of community formation can be captured in the form of a game. In this paper, we propose a greedy algorithm, namely, Weighted Graph Community Game (WGCG), in order to model the interactions among the self-interested nodes of the social network. The proposed algorithm employs the Shapley value mechanism to discover the inherent communities of the underlying social network. The experimental evaluation on the real-world and synthetic benchmark networks demonstrates that the performance of the proposed algorithm is superior to the state-of-the-art overlapping community detection algorithms.

Community detection for networks with unipartite and bipartite structure

Science.gov (United States)

Chang, Chang; Tang, Chao

2014-09-01

Finding community structures in networks is important in network science, technology, and applications. To date, most algorithms that aim to find community structures only focus either on unipartite or bipartite networks. A unipartite network consists of one set of nodes and a bipartite network consists of two nonoverlapping sets of nodes with only links joining the nodes in different sets. However, a third type of network exists, defined here as the mixture network. Just like a bipartite network, a mixture network also consists of two sets of nodes, but some nodes may simultaneously belong to two sets, which breaks the nonoverlapping restriction of a bipartite network. The mixture network can be considered as a general case, with unipartite and bipartite networks viewed as its limiting cases. A mixture network can represent not only all the unipartite and bipartite networks, but also a wide range of real-world networks that cannot be properly represented as either unipartite or bipartite networks in fields such as biology and social science. Based on this observation, we first propose a probabilistic model that can find modules in unipartite, bipartite, and mixture networks in a unified framework based on the link community model for a unipartite undirected network [B Ball et al (2011 Phys. Rev. E 84 036103)]. We test our algorithm on synthetic networks (both overlapping and nonoverlapping communities) and apply it to two real-world networks: a southern women bipartite network and a human transcriptional regulatory mixture network. The results suggest that our model performs well for all three types of networks, is competitive with other algorithms for unipartite or bipartite networks, and is applicable to real-world networks.

Improving the quality of learning in science through optimization of lesson study for learning community

Science.gov (United States)

Setyaningsih, S.

2018-03-01

Lesson Study for Learning Community is one of lecturer profession building system through collaborative and continuous learning study based on the principles of openness, collegiality, and mutual learning to build learning community in order to form professional learning community. To achieve the above, we need a strategy and learning method with specific subscription technique. This paper provides a description of how the quality of learning in the field of science can be improved by implementing strategies and methods accordingly, namely by applying lesson study for learning community optimally. Initially this research was focused on the study of instructional techniques. Learning method used is learning model Contextual teaching and Learning (CTL) and model of Problem Based Learning (PBL). The results showed that there was a significant increase in competence, attitudes, and psychomotor in the four study programs that were modelled. Therefore, it can be concluded that the implementation of learning strategies in Lesson study for Learning Community is needed to be used to improve the competence, attitude and psychomotor of science students.

Learning and Model-checking Networks of I/O Automata

DEFF Research Database (Denmark)

Mao, Hua; Jaeger, Manfred

2012-01-01

We introduce a new statistical relational learning (SRL) approach in which models for structured data, especially network data, are constructed as networks of communicating nite probabilistic automata. Leveraging existing automata learning methods from the area of grammatical inference, we can...... learn generic models for network entities in the form of automata templates. As is characteristic for SRL techniques, the abstraction level aorded by learning generic templates enables one to apply the learned model to new domains. A main benet of learning models based on nite automata lies in the fact...

Adaptive multi-resolution Modularity for detecting communities in networks

Science.gov (United States)

Chen, Shi; Wang, Zhi-Zhong; Bao, Mei-Hua; Tang, Liang; Zhou, Ji; Xiang, Ju; Li, Jian-Ming; Yi, Chen-He

2018-02-01

Community structure is a common topological property of complex networks, which attracted much attention from various fields. Optimizing quality functions for community structures is a kind of popular strategy for community detection, such as Modularity optimization. Here, we introduce a general definition of Modularity, by which several classical (multi-resolution) Modularity can be derived, and then propose a kind of adaptive (multi-resolution) Modularity that can combine the advantages of different Modularity. By applying the Modularity to various synthetic and real-world networks, we study the behaviors of the methods, showing the validity and advantages of the multi-resolution Modularity in community detection. The adaptive Modularity, as a kind of multi-resolution method, can naturally solve the first-type limit of Modularity and detect communities at different scales; it can quicken the disconnecting of communities and delay the breakup of communities in heterogeneous networks; and thus it is expected to generate the stable community structures in networks more effectively and have stronger tolerance against the second-type limit of Modularity.

Social network fragmentation and community health.

Science.gov (United States)

Chami, Goylette F; Ahnert, Sebastian E; Kabatereine, Narcis B; Tukahebwa, Edridah M

2017-09-05

Community health interventions often seek to intentionally destroy paths between individuals to prevent the spread of infectious diseases. Immunizing individuals through direct vaccination or the provision of health education prevents pathogen transmission and the propagation of misinformation concerning medical treatments. However, it remains an open question whether network-based strategies should be used in place of conventional field approaches to target individuals for medical treatment in low-income countries. We collected complete friendship and health advice networks in 17 rural villages of Mayuge District, Uganda. Here we show that acquaintance algorithms, i.e., selecting neighbors of randomly selected nodes, were systematically more efficient in fragmenting all networks than targeting well-established community roles, i.e., health workers, village government members, and schoolteachers. Additionally, community roles were not good proxy indicators of physical proximity to other households or connections to many sick people. We also show that acquaintance algorithms were effective in offsetting potential noncompliance with deworming treatments for 16,357 individuals during mass drug administration (MDA). Health advice networks were destroyed more easily than friendship networks. Only an average of 32% of nodes were removed from health advice networks to reduce the percentage of nodes at risk for refusing treatment in MDA to below 25%. Treatment compliance of at least 75% is needed in MDA to control human morbidity attributable to parasitic worms and progress toward elimination. Our findings point toward the potential use of network-based approaches as an alternative to role-based strategies for targeting individuals in rural health interventions.

An Overlapping Communities Detection Algorithm via Maxing Modularity in Opportunistic Networks

Directory of Open Access Journals (Sweden)

Gao Zhi-Peng

2016-01-01

Full Text Available Community detection in opportunistic networks has been a significant and hot issue, which is used to understand characteristics of networks through analyzing structure of it. Community is used to represent a group of nodes in a network where nodes inside the community have more internal connections than external connections. However, most of the existing community detection algorithms focus on binary networks or disjoint community detection. In this paper, we propose a novel algorithm via maxing modularity of communities (MMCto find overlapping community structure in opportunistic networks. It utilizes contact history of nodes to calculate the relation intensity between nodes. It finds nodes with high relation intensity as the initial community and extend the community with nodes of higher belong degree. The algorithm achieves a rapid and efficient overlapping community detection method by maxing the modularity of community continuously. The experiments prove that MMC is effective for uncovering overlapping communities and it achieves better performance than COPRA and Conductance.

Social Learning Network Analysis Model to Identify Learning Patterns Using Ontology Clustering Techniques and Meaningful Learning

Science.gov (United States)

Firdausiah Mansur, Andi Besse; Yusof, Norazah

2013-01-01

Clustering on Social Learning Network still not explored widely, especially when the network focuses on e-learning system. Any conventional methods are not really suitable for the e-learning data. SNA requires content analysis, which involves human intervention and need to be carried out manually. Some of the previous clustering techniques need…

Co-Operative Learning and Development Networks.

Science.gov (United States)

Hodgson, V.; McConnell, D.

1995-01-01

Discusses the theory, nature, and benefits of cooperative learning. Considers the Cooperative Learning and Development Network (CLDN) trial in the JITOL (Just in Time Open Learning) project and examines the relationship between theories about cooperative learning and the reality of a group of professionals participating in a virtual cooperative…

Clustering coefficient and community structure of bipartite networks

Science.gov (United States)

Zhang, Peng; Wang, Jinliang; Li, Xiaojia; Li, Menghui; Di, Zengru; Fan, Ying

2008-12-01

Many real-world networks display natural bipartite structure, where the basic cycle is a square. In this paper, with the similar consideration of standard clustering coefficient in binary networks, a definition of the clustering coefficient for bipartite networks based on the fraction of squares is proposed. In order to detect community structures in bipartite networks, two different edge clustering coefficients LC4 and LC3 of bipartite networks are defined, which are based on squares and triples respectively. With the algorithm of cutting the edge with the least clustering coefficient, communities in artificial and real world networks are identified. The results reveal that investigating bipartite networks based on the original structure can show the detailed properties that is helpful to get deep understanding about the networks.

Identifying influential user communities on the social network

Science.gov (United States)

Hu, Weishu; Gong, Zhiguo; Hou U, Leong; Guo, Jingzhi

2015-10-01

Nowadays social network services have been popularly used in electronic commerce systems. Users on the social network can develop different relationships based on their common interests and activities. In order to promote the business, it is interesting to explore hidden relationships among users developed on the social network. Such knowledge can be used to locate target users for different advertisements and to provide effective product recommendations. In this paper, we define and study a novel community detection problem that is to discover the hidden community structure in large social networks based on their common interests. We observe that the users typically pay more attention to those users who share similar interests, which enable a way to partition the users into different communities according to their common interests. We propose two algorithms to detect influential communities using common interests in large social networks efficiently and effectively. We conduct our experimental evaluation using a data set from Epinions, which demonstrates that our method achieves 4-11.8% accuracy improvement over the state-of-the-art method.

Networks and learning in game theory

NARCIS (Netherlands)

Kets, W.

2008-01-01

This work concentrates on two topics, networks and game theory, and learning in games. The first part of this thesis looks at network games and the role of incomplete information in such games. It is assumed that players are located on a network and interact with their neighbors in the network.

Using an academic-community partnership model and blended learning to advance community health nursing pedagogy.

Science.gov (United States)

Ezeonwu, Mabel; Berkowitz, Bobbie; Vlasses, Frances R

2014-01-01

This article describes a model of teaching community health nursing that evolved from a long-term partnership with a community with limited existing health programs. The partnership supported RN-BSN students' integration in the community and resulted in reciprocal gains for faculty, students and community members. Community clients accessed public health services as a result of the partnership. A blended learning approach that combines face-to-face interactions, service learning and online activities was utilized to enhance students' learning. Following classroom sessions, students actively participated in community-based educational process through comprehensive health needs assessments, planning and implementation of disease prevention and health promotion activities for community clients. Such active involvement in an underserved community deepened students' awareness of the fundamentals of community health practice. Students were challenged to view public health from a broader perspective while analyzing the impacts of social determinants of health on underserved populations. Through asynchronous online interactions, students synthesized classroom and community activities through critical thinking. This paper describes a model for teaching community health nursing that informs students' learning through blended learning, and meets the demands for community health nursing services delivery. © 2013 Wiley Periodicals, Inc.

Learning as Issue Framing in Agricultural Innovation Networks

Science.gov (United States)

Tisenkopfs, Talis; Kunda, Ilona; Šumane, Sandra

2014-01-01

Purpose: Networks are increasingly viewed as entities of learning and innovation in agriculture. In this article we explore learning as issue framing in two agricultural innovation networks. Design/methodology/approach: We combine frame analysis and social learning theories to analyse the processes and factors contributing to frame convergence and…

Community Tracking in a cMOOC and Nomadic Learner Behavior Identification on a Connectivist Rhizomatic Learning Network

Science.gov (United States)

Bozkurt, Aras; Honeychurch, Sarah; Caines, Autumm; Bali, Maha; Koutropoulos, Apostolos; Cormier, Dave

2016-01-01

This article contributes to the literature on connectivism, connectivist MOOCs (cMOOCs) and rhizomatic learning by examining participant interactions, community formation and nomadic learner behavior in a particular cMOOC, #rhizo15, facilitated for 6 weeks by Dave Cormier. It further focuses on what we can learn by observing Twitter interactions…

A Decomposition Algorithm for Learning Bayesian Network Structures from Data

DEFF Research Database (Denmark)

Zeng, Yifeng; Cordero Hernandez, Jorge

2008-01-01

It is a challenging task of learning a large Bayesian network from a small data set. Most conventional structural learning approaches run into the computational as well as the statistical problems. We propose a decomposition algorithm for the structure construction without having to learn...... the complete network. The new learning algorithm firstly finds local components from the data, and then recover the complete network by joining the learned components. We show the empirical performance of the decomposition algorithm in several benchmark networks....

Detecting the overlapping and hierarchical community structure in complex networks

International Nuclear Information System (INIS)

Lancichinetti, Andrea; Fortunato, Santo; Kertesz, Janos

2009-01-01

Many networks in nature, society and technology are characterized by a mesoscopic level of organization, with groups of nodes forming tightly connected units, called communities or modules, that are only weakly linked to each other. Uncovering this community structure is one of the most important problems in the field of complex networks. Networks often show a hierarchical organization, with communities embedded within other communities; moreover, nodes can be shared between different communities. Here, we present the first algorithm that finds both overlapping communities and the hierarchical structure. The method is based on the local optimization of a fitness function. Community structure is revealed by peaks in the fitness histogram. The resolution can be tuned by a parameter enabling different hierarchical levels of organization to be investigated. Tests on real and artificial networks give excellent results.

Vulnerability of R-MAT networks with communities

Directory of Open Access Journals (Sweden)

Nikolay Alexandrovich Kinash

2016-06-01

Full Text Available A generator R-MAT for modeling networks with different laws of link constructions within and between communities has been developed. Network attack simulations have been performed and pertinent robustness of diverse network combinations has been concluded.

Using a multi-state Learning Community as an implementation strategy for immediate postpartum long-acting reversible contraception.

Science.gov (United States)

DeSisto, Carla L; Estrich, Cameron; Kroelinger, Charlan D; Goodman, David A; Pliska, Ellen; Mackie, Christine N; Waddell, Lisa F; Rankin, Kristin M

2017-11-21

educational meetings, facilitation, promote network weaving, provide ongoing consultation, and distribute educational materials. Convening a multi-state learning collaborative is a promising approach for facilitating the implementation of new reimbursement policies for evidence-based practices complicated by systems challenges. By integrating several implementation strategies, the Learning Community serves as a meta-strategy for supporting implementation.

Sampling from complex networks with high community structures.

Science.gov (United States)

Salehi, Mostafa; Rabiee, Hamid R; Rajabi, Arezo

2012-06-01

In this paper, we propose a novel link-tracing sampling algorithm, based on the concepts from PageRank vectors, to sample from networks with high community structures. Our method has two phases; (1) Sampling the closest nodes to the initial nodes by approximating personalized PageRank vectors and (2) Jumping to a new community by using PageRank vectors and unknown neighbors. Empirical studies on several synthetic and real-world networks show that the proposed method improves the performance of network sampling compared to the popular link-based sampling methods in terms of accuracy and visited communities.

Learning Bayesian Networks with Incomplete Data by Augmentation

OpenAIRE

Adel, Tameem; de Campos, Cassio P.

2016-01-01

We present new algorithms for learning Bayesian networks from data with missing values using a data augmentation approach. An exact Bayesian network learning algorithm is obtained by recasting the problem into a standard Bayesian network learning problem without missing data. To the best of our knowledge, this is the first exact algorithm for this problem. As expected, the exact algorithm does not scale to large domains. We build on the exact method to create an approximate algorithm using a ...

Why STEM Learning Communities Work: The Development of Psychosocial Learning Factors through Social Interaction

Science.gov (United States)

Carrino, Stephanie Sedberry; Gerace, William J.

2016-01-01

STEM learning communities facilitate student academic success and persistence in science disciplines. This prompted us to explore the underlying factors that make learning communities successful. In this paper, we report findings from an illustrative case study of a 2-year STEM-based learning community designed to identify and describe these…

THE IMPACTS OF SOCIAL NETWORKING SITES IN HIGHER LEARNING

OpenAIRE

Mohd Ishak Bin Ismail; Ruzaini Bin Abdullah Arshah

2016-01-01

Social networking sites, a web-based application have permeated the boundary between personal lives and student lives. Nowadays, students in higher learning used social networking site such as Facebook to facilitate their learning through the academic collaboration which it further enhances students’ social capital. Social networking site has many advantages to improve students’ learning. To date, Facebook is the leading social networking sites at this time which it being widely used by stude...

Proceedings of the Neural Network Workshop for the Hanford Community

Energy Technology Data Exchange (ETDEWEB)

Keller, P.E.

1994-01-01

These proceedings were generated from a series of presentations made at the Neural Network Workshop for the Hanford Community. The abstracts and viewgraphs of each presentation are reproduced in these proceedings. This workshop was sponsored by the Computing and Information Sciences Department in the Molecular Science Research Center (MSRC) at the Pacific Northwest Laboratory (PNL). Artificial neural networks constitute a new information processing technology that is destined within the next few years, to provide the world with a vast array of new products. A major reason for this is that artificial neural networks are able to provide solutions to a wide variety of complex problems in a much simpler fashion than is possible using existing techniques. In recognition of these capabilities, many scientists and engineers are exploring the potential application of this new technology to their fields of study. An artificial neural network (ANN) can be a software simulation, an electronic circuit, optical system, or even an electro-chemical system designed to emulate some of the brain`s rudimentary structure as well as some of the learning processes that are believed to take place in the brain. For a very wide range of applications in science, engineering, and information technology, ANNs offer a complementary and potentially superior approach to that provided by conventional computing and conventional artificial intelligence. This is because, unlike conventional computers, which have to be programmed, ANNs essentially learn from experience and can be trained in a straightforward fashion to carry out tasks ranging from the simple to the highly complex.

Learning dynamic Bayesian networks with mixed variables

DEFF Research Database (Denmark)

Bøttcher, Susanne Gammelgaard

This paper considers dynamic Bayesian networks for discrete and continuous variables. We only treat the case, where the distribution of the variables is conditional Gaussian. We show how to learn the parameters and structure of a dynamic Bayesian network and also how the Markov order can be learned...

Designing Professional Learning Communities through Understanding the Beliefs of Learning

Science.gov (United States)

Ke, Jie; Kang, Rui; Liu, Di

2016-01-01

This study was designed to initiate the process of building professional development learning communities for pre-service math teachers through revealing those teachers' conceptions/beliefs of students' learning and their own learning in China. It examines Chinese pre-service math teachers' conceptions of student learning and their related…

75 FR 35881 - Smaller Learning Communities Program

Science.gov (United States)

2010-06-23

... Part II Department of Education Smaller Learning Communities Program; Notice #0;#0;Federal... EDUCATION Smaller Learning Communities Program Catalog of Federal Domestic Assistance (CFDA) Number: 84.215L. AGENCY: Office of Elementary and Secondary Education, Department of Education. ACTION: Notice of final...

Integrating Community into the Classroom: Community Gardening, Community Involvement, and Project-Based Learning.

Science.gov (United States)

Langhout, Regina Day; Rappaport, Julian; Simmons, Doretha

2002-01-01

Culturally relevant, ongoing project-based learning was facilitated in a predominantly African American urban elementary school via a community garden project. The project involved teachers, students, university members, and community members. This article evaluates the project through two classroom-community collaboration models, noting common…

Global and local targeted immunization in networks with community structure

International Nuclear Information System (INIS)

Yan, Shu; Tang, Shaoting; Pei, Sen; Zheng, Zhiming; Fang, Wenyi

2015-01-01

Immunization plays an important role in the field of epidemic spreading in complex networks. In previous studies, targeted immunization has been proved to be an effective strategy. However, when extended to networks with community structure, it is unknown whether the superior strategy is to vaccinate the nodes who have the most connections in the entire network (global strategy), or those in the original community where epidemic starts to spread (local strategy). In this work, by using both analytic approaches and simulations, we observe that the answer depends on the closeness between communities. If communities are tied closely, the global strategy is superior to the local strategy. Otherwise, the local targeted immunization is advantageous. The existence of a transitional value of closeness implies that we should adopt different strategies. Furthermore, we extend our investigation from two-community networks to multi-community networks. We consider the mode of community connection and the location of community where epidemic starts to spread. Both simulation results and theoretical predictions show that local strategy is a better option for immunization in most cases. But if the epidemic begins from a core community, global strategy is superior in some cases. (paper)

Malware Propagation and Prevention Model for Time-Varying Community Networks within Software Defined Networks

Directory of Open Access Journals (Sweden)

Lan Liu

2017-01-01

Full Text Available As the adoption of Software Defined Networks (SDNs grows, the security of SDN still has several unaddressed limitations. A key network security research area is in the study of malware propagation across the SDN-enabled networks. To analyze the spreading processes of network malware (e.g., viruses in SDN, we propose a dynamic model with a time-varying community network, inspired by research models on the spread of epidemics in complex networks across communities. We assume subnets of the network as communities and links that are dense in subnets but sparse between subnets. Using numerical simulation and theoretical analysis, we find that the efficiency of network malware propagation in this model depends on the mobility rate q of the nodes between subnets. We also find that there exists a mobility rate threshold qc. The network malware will spread in the SDN when the mobility rate q>qc. The malware will survive when q>qc and perish when qnetwork malware and provide a theoretical basis to reduce and prevent network security incidents.

Deep learning in neural networks: an overview.

Science.gov (United States)

Schmidhuber, Jürgen

2015-01-01

In recent years, deep artificial neural networks (including recurrent ones) have won numerous contests in pattern recognition and machine learning. This historical survey compactly summarizes relevant work, much of it from the previous millennium. Shallow and Deep Learners are distinguished by the depth of their credit assignment paths, which are chains of possibly learnable, causal links between actions and effects. I review deep supervised learning (also recapitulating the history of backpropagation), unsupervised learning, reinforcement learning & evolutionary computation, and indirect search for short programs encoding deep and large networks.

Service-learning and learning communities: two innovative school projects that are mutually enriched

Directory of Open Access Journals (Sweden)

Carmen ÁLVAREZ ÁLVAREZ

2015-12-01

Full Text Available This article reflects on the interrelationships that exist between two educational projects of today: service-learning (ApS and learning communities (CdA. The ApS is an educational methodology applied worldwide where a single project combines a learning based on experience with the implementation of a service to the community. CdA is a school transformation project to achieve that the information society does not exclude any person, constituting a reality in more than one hundred and ninety schools in Spain and Latin America. Between the two, it is possible to show differences, especially in what refers to its theoretical substrates, but in actual teaching practice in schools there is some harmony, particularly in the so closely that they cultivate both projects with the school community. Therefore, we conclude that service-learning and learning communities can occur as two innovative and relevant today projects which can be mutually enriching: because for both the approach school-community-environment and volunteering is essential.

Reversal Learning in Humans and Gerbils: Dynamic Control Network Facilitates Learning.

Science.gov (United States)

Jarvers, Christian; Brosch, Tobias; Brechmann, André; Woldeit, Marie L; Schulz, Andreas L; Ohl, Frank W; Lommerzheim, Marcel; Neumann, Heiko

2016-01-01

Biologically plausible modeling of behavioral reinforcement learning tasks has seen great improvements over the past decades. Less work has been dedicated to tasks involving contingency reversals, i.e., tasks in which the original behavioral goal is reversed one or multiple times. The ability to adjust to such reversals is a key element of behavioral flexibility. Here, we investigate the neural mechanisms underlying contingency-reversal tasks. We first conduct experiments with humans and gerbils to demonstrate memory effects, including multiple reversals in which subjects (humans and animals) show a faster learning rate when a previously learned contingency re-appears. Motivated by recurrent mechanisms of learning and memory for object categories, we propose a network architecture which involves reinforcement learning to steer an orienting system that monitors the success in reward acquisition. We suggest that a model sensory system provides feature representations which are further processed by category-related subnetworks which constitute a neural analog of expert networks. Categories are selected dynamically in a competitive field and predict the expected reward. Learning occurs in sequentialized phases to selectively focus the weight adaptation to synapses in the hierarchical network and modulate their weight changes by a global modulator signal. The orienting subsystem itself learns to bias the competition in the presence of continuous monotonic reward accumulation. In case of sudden changes in the discrepancy of predicted and acquired reward the activated motor category can be switched. We suggest that this subsystem is composed of a hierarchically organized network of dis-inhibitory mechanisms, dubbed a dynamic control network (DCN), which resembles components of the basal ganglia. The DCN selectively activates an expert network, corresponding to the current behavioral strategy. The trace of the accumulated reward is monitored such that large sudden

Partner network communities – a resource of universities’ activities

Directory of Open Access Journals (Sweden)

Romm Mark V.

2016-01-01

Full Text Available The network activity is not only part and parcel of the modern university, but it also demonstrates the level of its success. There appeared an urgent need for understanding the nature of universities’ network interactions and finding the most effective models of their network cooperation. The article analyzes partnership network communities with higher educational establishments (universities’ participation, which are being actively created nowadays. The conditions for successful network activities of a university in scientific, academic and professional network communities are presented.

Identifying Gatekeepers in Online Learning Networks

Science.gov (United States)

Gursakal, Necmi; Bozkurt, Aras

2017-01-01

The rise of the networked society has not only changed our perceptions but also the definitions, roles, processes and dynamics of online learning networks. From offline to online worlds, networks are everywhere and gatekeepers are an important entity in these networks. In this context, the purpose of this paper is to explore gatekeeping and…

Epistemic Communities, Situated Learning and Open Source Software Development

DEFF Research Database (Denmark)

Edwards, Kasper

2001-01-01

This paper analyses open source software (OSS) development as an epistemic community where each individual project is perceived as a single epistemic community. OSS development is a learning process where the involved parties contribute to, and learn from the community. It is discovered that theory...... of epistemic communities does indeed contribute to the understanding of open source software development. But, the important learning process of open source software development is not readily explained. The paper then introduces situated learning and legitimate peripheral participation as theoretical...

Robust Learning of Fixed-Structure Bayesian Networks

OpenAIRE

Diakonikolas, Ilias; Kane, Daniel; Stewart, Alistair

2016-01-01

We investigate the problem of learning Bayesian networks in an agnostic model where an $\\epsilon$-fraction of the samples are adversarially corrupted. Our agnostic learning model is similar to -- in fact, stronger than -- Huber's contamination model in robust statistics. In this work, we study the fully observable Bernoulli case where the structure of the network is given. Even in this basic setting, previous learning algorithms either run in exponential time or lose dimension-dependent facto...

Extraction of business relationships in supply networks using statistical learning theory.

Science.gov (United States)

Zuo, Yi; Kajikawa, Yuya; Mori, Junichiro

2016-06-01

Supply chain management represents one of the most important scientific streams of operations research. The supply of energy, materials, products, and services involves millions of transactions conducted among national and local business enterprises. To deliver efficient and effective support for supply chain design and management, structural analyses and predictive models of customer-supplier relationships are expected to clarify current enterprise business conditions and to help enterprises identify innovative business partners for future success. This article presents the outcomes of a recent structural investigation concerning a supply network in the central area of Japan. We investigated the effectiveness of statistical learning theory to express the individual differences of a supply chain of enterprises within a certain business community using social network analysis. In the experiments, we employ support vector machine to train a customer-supplier relationship model on one of the main communities extracted from a supply network in the central area of Japan. The prediction results reveal an F-value of approximately 70% when the model is built by using network-based features, and an F-value of approximately 77% when the model is built by using attribute-based features. When we build the model based on both, F-values are improved to approximately 82%. The results of this research can help to dispel the implicit design space concerning customer-supplier relationships, which can be explored and refined from detailed topological information provided by network structures rather than from traditional and attribute-related enterprise profiles. We also investigate and discuss differences in the predictive accuracy of the model for different sizes of enterprises and types of business communities.

Extraction of business relationships in supply networks using statistical learning theory

Directory of Open Access Journals (Sweden)

Yi Zuo

2016-06-01

Full Text Available Supply chain management represents one of the most important scientific streams of operations research. The supply of energy, materials, products, and services involves millions of transactions conducted among national and local business enterprises. To deliver efficient and effective support for supply chain design and management, structural analyses and predictive models of customer–supplier relationships are expected to clarify current enterprise business conditions and to help enterprises identify innovative business partners for future success. This article presents the outcomes of a recent structural investigation concerning a supply network in the central area of Japan. We investigated the effectiveness of statistical learning theory to express the individual differences of a supply chain of enterprises within a certain business community using social network analysis. In the experiments, we employ support vector machine to train a customer–supplier relationship model on one of the main communities extracted from a supply network in the central area of Japan. The prediction results reveal an F-value of approximately 70% when the model is built by using network-based features, and an F-value of approximately 77% when the model is built by using attribute-based features. When we build the model based on both, F-values are improved to approximately 82%. The results of this research can help to dispel the implicit design space concerning customer–supplier relationships, which can be explored and refined from detailed topological information provided by network structures rather than from traditional and attribute-related enterprise profiles. We also investigate and discuss differences in the predictive accuracy of the model for different sizes of enterprises and types of business communities.

International learning communities for global and localcitizenship

Directory of Open Access Journals (Sweden)

Hana Cervinkova

2011-10-01

Full Text Available In this paper, I describe our ongoing international project in engaged educationalethnography and participatory action research with young adults and consider itsrelevance for a discussion on the community-building role of adult education in aglobalized context. I use the example of our case study to suggest that adult educatorscan generate viable communities by creating learning spaces that nurture criticalconsciousness, a sense of agency, participation and social solidarity amonginternationally and culturally diverse young adult learners. Furthermore, I argue thatparticipation in international learning communities formed through this educationalprocess can potentially help young adults become locally and globally engaged citizens.International learning communities for global citizenship thus present a proposition forconceptualizing the vital role of adult community education in supporting democraticglobal and local citizenship in a world defined in terms of cross-cultural and longdistanceencounters in the formation of culture.

Statistical and machine learning approaches for network analysis

CERN Document Server

Dehmer, Matthias

2012-01-01

Explore the multidisciplinary nature of complex networks through machine learning techniques Statistical and Machine Learning Approaches for Network Analysis provides an accessible framework for structurally analyzing graphs by bringing together known and novel approaches on graph classes and graph measures for classification. By providing different approaches based on experimental data, the book uniquely sets itself apart from the current literature by exploring the application of machine learning techniques to various types of complex networks. Comprised of chapters written by internation

A framework for detecting communities of unbalanced sizes in networks

Science.gov (United States)

Žalik, Krista Rizman; Žalik, Borut

2018-01-01

Community detection in large networks has been a focus of recent research in many of fields, including biology, physics, social sciences, and computer science. Most community detection methods partition the entire network into communities, groups of nodes that have many connections within communities and few connections between them and do not identify different roles that nodes can have in communities. We propose a community detection model that integrates more different measures that can fast identify communities of different sizes and densities. We use node degree centrality, strong similarity with one node from community, maximal similarity of node to community, compactness of communities and separation between communities. Each measure has its own strength and weakness. Thus, combining different measures can benefit from the strengths of each one and eliminate encountered problems of using an individual measure. We present a fast local expansion algorithm for uncovering communities of different sizes and densities and reveals rich information on input networks. Experimental results show that the proposed algorithm is better or as effective as the other community detection algorithms for both real-world and synthetic networks while it requires less time.

Stylized facts in social networks: Community-based static modeling

Science.gov (United States)

Jo, Hang-Hyun; Murase, Yohsuke; Török, János; Kertész, János; Kaski, Kimmo

2018-06-01

The past analyses of datasets of social networks have enabled us to make empirical findings of a number of aspects of human society, which are commonly featured as stylized facts of social networks, such as broad distributions of network quantities, existence of communities, assortative mixing, and intensity-topology correlations. Since the understanding of the structure of these complex social networks is far from complete, for deeper insight into human society more comprehensive datasets and modeling of the stylized facts are needed. Although the existing dynamical and static models can generate some stylized facts, here we take an alternative approach by devising a community-based static model with heterogeneous community sizes and larger communities having smaller link density and weight. With these few assumptions we are able to generate realistic social networks that show most stylized facts for a wide range of parameters, as demonstrated numerically and analytically. Since our community-based static model is simple to implement and easily scalable, it can be used as a reference system, benchmark, or testbed for further applications.

Evolution properties of the community members for dynamic networks

Science.gov (United States)

Yang, Kai; Guo, Qiang; Li, Sheng-Nan; Han, Jing-Ti; Liu, Jian-Guo

2017-03-01

The collective behaviors of community members for dynamic social networks are significant for understanding evolution features of communities. In this Letter, we empirically investigate the evolution properties of the new community members for dynamic networks. Firstly, we separate data sets into different slices, and analyze the statistical properties of new members as well as communities they joined in for these data sets. Then we introduce a parameter φ to describe community evolution between different slices and investigate the dynamic community properties of the new community members. The empirical analyses for the Facebook, APS, Enron and Wiki data sets indicate that both the number of new members and joint communities increase, the ratio declines rapidly and then becomes stable over time, and most of the new members will join in the small size communities that is s ≤ 10. Furthermore, the proportion of new members in existed communities decreases firstly and then becomes stable and relatively small for these data sets. Our work may be helpful for deeply understanding the evolution properties of community members for social networks.

Community Structure in Online Collegiate Social Networks

Science.gov (United States)

Traud, Amanda; Kelsic, Eric; Mucha, Peter; Porter, Mason

2009-03-01

Online social networking sites have become increasingly popular with college students. The networks we studied are defined through ``friendships'' indicated by Facebook users from UNC, Oklahoma, Caltech, Georgetown, and Princeton. We apply the tools of network science to study the Facebook networks from these five different universities at a single point in time. We investigate each single-institution network's community structure, which we obtain through partitioning the graph using an eigenvector method. We use both graphical and quantitative tools, including pair-counting methods, which we interpret through statistical analysis and permutation tests to measure the correlations between the network communities and a set of characteristics given by each user (residence, class year, major, and high school). We also analyze the single gender subsets of these networks, and the impact of missing demographical data. Our study allows us to compare the online social networks for the five schools as well as infer differences in offline social interactions. At the schools studied, we were able to define which characteristics of the Facebook users correlate best with friendships.

Thermodynamic efficiency of learning a rule in neural networks

Science.gov (United States)

Goldt, Sebastian; Seifert, Udo

2017-11-01

Biological systems have to build models from their sensory input data that allow them to efficiently process previously unseen inputs. Here, we study a neural network learning a binary classification rule for these inputs from examples provided by a teacher. We analyse the ability of the network to apply the rule to new inputs, that is to generalise from past experience. Using stochastic thermodynamics, we show that the thermodynamic costs of the learning process provide an upper bound on the amount of information that the network is able to learn from its teacher for both batch and online learning. This allows us to introduce a thermodynamic efficiency of learning. We analytically compute the dynamics and the efficiency of a noisy neural network performing online learning in the thermodynamic limit. In particular, we analyse three popular learning algorithms, namely Hebbian, Perceptron and AdaTron learning. Our work extends the methods of stochastic thermodynamics to a new type of learning problem and might form a suitable basis for investigating the thermodynamics of decision-making.

Learning from Community: Agenda for Citizenship Education

Science.gov (United States)

Ghosh, Sujay

2015-01-01

Citizenship is about individual's membership in the socio-political community. Education for citizenship conceives issues such as quality education, learning society and inclusion. Educational thinking in India has long valued community as a learning resource. With empirical experiences drawn from the programme of "Ecology and Natural…

Bullying in Virtual Learning Communities.

Science.gov (United States)

Nikiforos, Stefanos; Tzanavaris, Spyros; Kermanidis, Katia Lida

2017-01-01

Bullying through the internet has been investigated and analyzed mainly in the field of social media. In this paper, it is attempted to analyze bullying in the Virtual Learning Communities using Natural Language Processing (NLP) techniques, mainly in the context of sociocultural learning theories. Therefore four case studies took place. We aim to apply NLP techniques to speech analysis on communication data of online communities. Emphasis is given on qualitative data, taking into account the subjectivity of the collaborative activity. Finally, this is the first time such type of analysis is attempted on Greek data.

An exploration of fetish social networks and communities

OpenAIRE

Fay, Damien; Haddadi, Hamed; Seto, Michael C.; Wang, Han; Kling, Christoph Carl

2015-01-01

Online Social Networks (OSNs) provide a venue for virtual interactions and relationships between individuals. In some communities, OSNs also facilitate arranging online meetings and relationships. FetLife, the worlds largest anonymous social network for the BDSM, fetish and kink communities, provides a unique example of an OSN that serves as an interaction space, community organizing tool, and sexual market. In this paper, we present a first look at the characteristics of European members of ...

Transitioning from learning healthcare systems to learning health care communities.

Science.gov (United States)

Mullins, C Daniel; Wingate, La'Marcus T; Edwards, Hillary A; Tofade, Toyin; Wutoh, Anthony

2018-02-26

The learning healthcare system (LHS) model framework has three core, foundational components. These include an infrastructure for health-related data capture, care improvement targets and a supportive policy environment. Despite progress in advancing and implementing LHS approaches, low levels of participation from patients and the public have hampered the transformational potential of the LHS model. An enhanced vision of a community-engaged LHS redesign would focus on the provision of health care from the patient and community perspective to complement the healthcare system as the entity that provides the environment for care. Addressing the LHS framework implementation challenges and utilizing community levers are requisite components of a learning health care community model, version two of the LHS archetype.

Saraniyadhamma Community knowledge Incubator area

Directory of Open Access Journals (Sweden)

Siripong Arundechachai

2017-03-01

Full Text Available The purposes of this research were to 1 study the situation of the community knowledge incubator area at the past to the present time in Banhad,Tambon banhad, Amphoe banhad Changwat KhonKaen, 2 study guidelines Buddhadhamma “Saraniyadhamma” revised by Community knowledge application Banhad, Tambon banhad, Amphoe banhad Changwat KhonKaen, 3 study workflow of Saraniyadhamma that led to the creation of the network community knowledge incubator area together with another community. The target groups used in this research of the purposive sampling family farmers of 10, in Tambon banhad,Amphoe banhad Changwat KhonKaen. the Qualitative research.was used in this Study The results showed that 1 diversing issues in the Community live action of the relationships or occupations experience can be passed down, as well as the risk of loss the relationships between the people and people, people and supernatural. After people and nature lost in the community, but thay Continue to Perform, because community has strengths given the importance of all, to themselves, to others, generous, generosity, mounting traditions, Led to the creation Community Knowledge Incubator 2 adopting Buddhism’s “Saraniyadhamma 6” that applied to community Knowledge Incubator by giving to make immunity community. Strong The six fetures, were Principle 1: Metta-kayakamma, feature on sacrifiction, unity and synergy. Principle 2: Metta-manokamma, feature on mercifulness, collective sacrification. Principle 3: Metta-kayakamma, feature on good things, speak well, good action. Principle 4: Sadharana-bhogi, feature on humane society, mutual respect. Principle 5 Sila-samannata, feature on, follow the rules of society. Principle 5 Metta-manokamma feature on rationality, listening to the opinion of others. It found that there were process-driven learning and following six rules of saraniyadhamma, and immunity system, risk Decoupled. 3 Networks Saraniyadhamma learnt together with other

Covariance, correlation matrix, and the multiscale community structure of networks.

Science.gov (United States)

Shen, Hua-Wei; Cheng, Xue-Qi; Fang, Bin-Xing

2010-07-01

Empirical studies show that real world networks often exhibit multiple scales of topological descriptions. However, it is still an open problem how to identify the intrinsic multiple scales of networks. In this paper, we consider detecting the multiscale community structure of network from the perspective of dimension reduction. According to this perspective, a covariance matrix of network is defined to uncover the multiscale community structure through the translation and rotation transformations. It is proved that the covariance matrix is the unbiased version of the well-known modularity matrix. We then point out that the translation and rotation transformations fail to deal with the heterogeneous network, which is very common in nature and society. To address this problem, a correlation matrix is proposed through introducing the rescaling transformation into the covariance matrix. Extensive tests on real world and artificial networks demonstrate that the correlation matrix significantly outperforms the covariance matrix, identically the modularity matrix, as regards identifying the multiscale community structure of network. This work provides a novel perspective to the identification of community structure and thus various dimension reduction methods might be used for the identification of community structure. Through introducing the correlation matrix, we further conclude that the rescaling transformation is crucial to identify the multiscale community structure of network, as well as the translation and rotation transformations.

Computer-facilitated community building for E-learning

NARCIS (Netherlands)

Nijholt, Antinus; Petrushin, V.; Kommers, Petrus A.M.; Kinshuk, X.; Galeev, I.

2002-01-01

This is a short survey of tools and ideas that are helpful for community building for E-learning. The underlying assumption in the survey is that community building for students and teachers in a joint learning and teaching situation is useful. Especially student-student interaction in student life

Proposing Community-Based Learning in the Marketing Curriculum

Science.gov (United States)

Cadwallader, Susan; Atwong, Catherine; Lebard, Aubrey

2013-01-01

Community service and service learning (CS&SL) exposes students to the business practice of giving back to society while reinforcing classroom learning in an applied real-world setting. However, does the CS&SL format provide a better means of instilling the benefits of community service among marketing students than community-based…

Holding the Reins of the Professional Learning Community: Eight Themes from Research on Principals' Perceptions of Professional Learning Communities

Science.gov (United States)

Cranston, Jerome

2009-01-01

Using a naturalistic inquiry approach and thematic analysis, this paper outlines the findings of a research study that examined 12 Manitoba principals' conceptions of professional learning communities. The study found that these principals consider the development of professional learning communities to be a normative imperative within the…

Mixture models with entropy regularization for community detection in networks

Science.gov (United States)

Chang, Zhenhai; Yin, Xianjun; Jia, Caiyan; Wang, Xiaoyang

2018-04-01

Community detection is a key exploratory tool in network analysis and has received much attention in recent years. NMM (Newman's mixture model) is one of the best models for exploring a range of network structures including community structure, bipartite and core-periphery structures, etc. However, NMM needs to know the number of communities in advance. Therefore, in this study, we have proposed an entropy regularized mixture model (called EMM), which is capable of inferring the number of communities and identifying network structure contained in a network, simultaneously. In the model, by minimizing the entropy of mixing coefficients of NMM using EM (expectation-maximization) solution, the small clusters contained little information can be discarded step by step. The empirical study on both synthetic networks and real networks has shown that the proposed model EMM is superior to the state-of-the-art methods.

Cluster synchronization for directed community networks via pinning partial schemes

International Nuclear Information System (INIS)

Hu Cheng; Jiang Haijun

2012-01-01

Highlights: ► Cluster synchronization for directed community networks is proposed by pinning partial schemes. ► Each community is considered as a whole. ► Several novel pinning criteria are derived based on the information of communities. ► A numerical example with simulation is provided. - Abstract: In this paper, we focus on driving a class of directed networks to achieve cluster synchronization by pinning schemes. The desired cluster synchronization states are no longer decoupled orbits but a set of un-decoupled trajectories. Each community is considered as a whole and the synchronization criteria are derived based on the information of communities. Several pinning schemes including feedback control and adaptive strategy are proposed to select controlled communities by analyzing the information of each community such as indegrees and outdegrees. In all, this paper answers several challenging problems in pinning control of directed community networks: (1) What communities should be chosen as controlled candidates? (2) How many communities are needed to be controlled? (3) How large should the control gains be used in a given community network to achieve cluster synchronization? Finally, an example with numerical simulations is given to demonstrate the effectiveness of the theoretical results.

Integrative and Deep Learning through a Learning Community: A Process View of Self

Science.gov (United States)

Mahoney, Sandra; Schamber, Jon

2011-01-01

This study investigated deep learning produced in a community of general education courses. Student speeches on liberal education were analyzed for discovering a grounded theory of ideas about self. The study found that learning communities cultivate deep, integrative learning that makes the value of a liberal education relevant to students.…

Learning Local Components to Understand Large Bayesian Networks

DEFF Research Database (Denmark)

Zeng, Yifeng; Xiang, Yanping; Cordero, Jorge

2009-01-01

(domain experts) to extract accurate information from a large Bayesian network due to dimensional difficulty. We define a formulation of local components and propose a clustering algorithm to learn such local components given complete data. The algorithm groups together most inter-relevant attributes......Bayesian networks are known for providing an intuitive and compact representation of probabilistic information and allowing the creation of models over a large and complex domain. Bayesian learning and reasoning are nontrivial for a large Bayesian network. In parallel, it is a tough job for users...... in a domain. We evaluate its performance on three benchmark Bayesian networks and provide results in support. We further show that the learned components may represent local knowledge more precisely in comparison to the full Bayesian networks when working with a small amount of data....

Evolving autonomous learning in cognitive networks.

Science.gov (United States)

Sheneman, Leigh; Hintze, Arend

2017-12-01

There are two common approaches for optimizing the performance of a machine: genetic algorithms and machine learning. A genetic algorithm is applied over many generations whereas machine learning works by applying feedback until the system meets a performance threshold. These methods have been previously combined, particularly in artificial neural networks using an external objective feedback mechanism. We adapt this approach to Markov Brains, which are evolvable networks of probabilistic and deterministic logic gates. Prior to this work MB could only adapt from one generation to the other, so we introduce feedback gates which augment their ability to learn during their lifetime. We show that Markov Brains can incorporate these feedback gates in such a way that they do not rely on an external objective feedback signal, but instead can generate internal feedback that is then used to learn. This results in a more biologically accurate model of the evolution of learning, which will enable us to study the interplay between evolution and learning and could be another step towards autonomously learning machines.

Game Theoretical Analysis on Cooperation Stability and Incentive Effectiveness in Community Networks.

Science.gov (United States)

Song, Kaida; Wang, Rui; Liu, Yi; Qian, Depei; Zhang, Han; Cai, Jihong

2015-01-01

Community networks, the distinguishing feature of which is membership admittance, appear on P2P networks, social networks, and conventional Web networks. Joining the network costs money, time or network bandwidth, but the individuals get access to special resources owned by the community in return. The prosperity and stability of the community are determined by both the policy of admittance and the attraction of the privileges gained by joining. However, some misbehaving users can get the dedicated resources with some illicit and low-cost approaches, which introduce instability into the community, a phenomenon that will destroy the membership policy. In this paper, we analyze on the stability using game theory on such a phenomenon. We propose a game-theoretical model of stability analysis in community networks and provide conditions for a stable community. We then extend the model to analyze the effectiveness of different incentive policies, which could be used when the community cannot maintain its members in certain situations. Then we verify those models through a simulation. Finally, we discuss several ways to promote community network's stability by adjusting the network's properties and give some proposal on the designs of these types of networks from the points of game theory and stability.

Game Theoretical Analysis on Cooperation Stability and Incentive Effectiveness in Community Networks.

Directory of Open Access Journals (Sweden)

Kaida Song

Full Text Available Community networks, the distinguishing feature of which is membership admittance, appear on P2P networks, social networks, and conventional Web networks. Joining the network costs money, time or network bandwidth, but the individuals get access to special resources owned by the community in return. The prosperity and stability of the community are determined by both the policy of admittance and the attraction of the privileges gained by joining. However, some misbehaving users can get the dedicated resources with some illicit and low-cost approaches, which introduce instability into the community, a phenomenon that will destroy the membership policy. In this paper, we analyze on the stability using game theory on such a phenomenon. We propose a game-theoretical model of stability analysis in community networks and provide conditions for a stable community. We then extend the model to analyze the effectiveness of different incentive policies, which could be used when the community cannot maintain its members in certain situations. Then we verify those models through a simulation. Finally, we discuss several ways to promote community network's stability by adjusting the network's properties and give some proposal on the designs of these types of networks from the points of game theory and stability.

Local community detection as pattern restoration by attractor dynamics of recurrent neural networks.

Science.gov (United States)

Okamoto, Hiroshi

2016-08-01

Densely connected parts in networks are referred to as "communities". Community structure is a hallmark of a variety of real-world networks. Individual communities in networks form functional modules of complex systems described by networks. Therefore, finding communities in networks is essential to approaching and understanding complex systems described by networks. In fact, network science has made a great deal of effort to develop effective and efficient methods for detecting communities in networks. Here we put forward a type of community detection, which has been little examined so far but will be practically useful. Suppose that we are given a set of source nodes that includes some (but not all) of "true" members of a particular community; suppose also that the set includes some nodes that are not the members of this community (i.e., "false" members of the community). We propose to detect the community from this "imperfect" and "inaccurate" set of source nodes using attractor dynamics of recurrent neural networks. Community detection by the proposed method can be viewed as restoration of the original pattern from a deteriorated pattern, which is analogous to cue-triggered recall of short-term memory in the brain. We demonstrate the effectiveness of the proposed method using synthetic networks and real social networks for which correct communities are known. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Healthcare Learning Community and Student Retention

Science.gov (United States)

Johnson, Sherryl W.

2014-01-01

Teaching, learning, and retention processes have evolved historically to include multifaceted techniques beyond the traditional lecture. This article presents related results of a study using a healthcare learning community in a southwest Georgia university. The value of novel techniques and tools in promoting student learning and retention…

Network communities within and across borders

OpenAIRE

Cerina, Federica; Chessa, Alessandro; Pammolli, Fabio; Riccaboni, Massimo

2014-01-01

We investigate the impact of borders on the topology of spatially embedded networks. Indeed territorial subdivisions and geographical borders significantly hamper the geographical span of networks thus playing a key role in the formation of network communities. This is especially important in scientific and technological policy-making, highlighting the interplay between pressure for the internationalization to lead towards a global innovation system and the administrative borders imposed by t...

A Professional Learning Community Approach

African Journals Online (AJOL)

This paper provides insights into how Life Sciences teachers in the Eastern Cape can be supported through professional learning communities (PLCs) as a potential approach to enhancing their biodiversity knowledge. PLCs are communities that provide the setting and necessary support for groups of classroom teachers to ...

Dynamical community structure of populations evolving on genotype networks

International Nuclear Information System (INIS)

Capitán, José A.; Aguirre, Jacobo; Manrubia, Susanna

2015-01-01

Neutral evolutionary dynamics of replicators occurs on large and heterogeneous networks of genotypes. These networks, formed by all genotypes that yield the same phenotype, have a complex architecture that conditions the molecular composition of populations and their movements on genome spaces. Here we consider as an example the case of populations evolving on RNA secondary structure neutral networks and study the community structure of the network revealed through dynamical properties of the population at equilibrium and during adaptive transients. We unveil a rich hierarchical community structure that, eventually, can be traced back to the non-trivial relationship between RNA secondary structure and sequence composition. We demonstrate that usual measures of modularity that only take into account the static, topological structure of networks, cannot identify the community structure disclosed by population dynamics

Building international collaborative capacity: contributions of community psychologists to a European network.

Science.gov (United States)

García-Ramírez, Manuel; Paloma, Virginia; Suarez-Balcazar, Yolanda; Balcazar, Fabricio

2009-09-01

Europe is in the process of building a more participative, just, and inclusive European Union. The European Social Fund, which is an initiative developed to actively promote multinational partnerships that address pressing social issues, is a good example of the European transformation. This article describes the steps taken to develop and evaluate the activities of an international network promoting collaborative capacity among regional partners involved in the prevention of labor discrimination toward immigrants in three European countries-Spain, Belgium, and Italy. An international team of community psychologists proposed an empowering approach to assess the collaborative capacity of the network. This approach consisted of three steps: (1) establishing a collaborative relationship among partners, (2) building collaborative capacity, and (3) evaluating the collaborative capacity of the network. We conclude with lessons learned from the process and provide recommendations for addressing the challenges inherent in international collaboration processes.

A professional learning community model: a case study of primary teachers community in west Bandung

Science.gov (United States)

Sari, A.; Suryadi, D.; Syaodih, E.

2018-05-01

The purpose of this study is to provide an alternative model of professional learning community for primary school teachers in improving the knowledge and professional skills. This study is a qualitative research with case study method with data collection is an interview, observation and document and triangulation technique for validation data that focuses on thirteen people 5th grade elementary school teacher. The results showed that by joining a professional learning community, teachers can share both experience and knowledge to other colleagues so that they can be able to continue to improve and enhance the quality of their learning. This happens because of the reflection done together before, during and after the learning activities. It was also revealed that by learning in a professional learning community, teachers can learn in their own way, according to need, and can collaborate with their colleagues in improving the effectiveness of learning. Based on the implementation of professional learning community primary school teachers can be concluded that teachers can develop the curriculum, the students understand the development, overcome learning difficulties faced by students and can make learning design more effective and efficient.

Efficient learning strategy of Chinese characters based on network approach.

Directory of Open Access Journals (Sweden)

Xiaoyong Yan

Full Text Available We develop an efficient learning strategy of Chinese characters based on the network of the hierarchical structural relations between Chinese characters. A more efficient strategy is that of learning the same number of useful Chinese characters in less effort or time. We construct a node-weighted network of Chinese characters, where character usage frequencies are used as node weights. Using this hierarchical node-weighted network, we propose a new learning method, the distributed node weight (DNW strategy, which is based on a new measure of nodes' importance that considers both the weight of the nodes and its location in the network hierarchical structure. Chinese character learning strategies, particularly their learning order, are analyzed as dynamical processes over the network. We compare the efficiency of three theoretical learning methods and two commonly used methods from mainstream Chinese textbooks, one for Chinese elementary school students and the other for students learning Chinese as a second language. We find that the DNW method significantly outperforms the others, implying that the efficiency of current learning methods of major textbooks can be greatly improved.

Learning Bayesian networks for discrete data

KAUST Repository

Liang, Faming; Zhang, Jian

2009-01-01

Bayesian networks have received much attention in the recent literature. In this article, we propose an approach to learn Bayesian networks using the stochastic approximation Monte Carlo (SAMC) algorithm. Our approach has two nice features. Firstly

Learning to walk the community of practice tightrope

Directory of Open Access Journals (Sweden)

Denise Edgar

2016-11-01

Full Text Available Background: The Community of Practice Research was established as a new local health district service initiative. The community comprises novice and experienced multidisciplinary health researchers. Aims: This paper reflects our experience of being Community of Practice Research members and aims to explore the practice development principles aligned to the purpose, progress and outcomes of this community. Conclusions: The journey is compared to walking a tightrope from the beginning to the end. Success in moving forward is attributed to positive leadership and group dynamics enabling a supportive environment. This environment allowed for different types of learning: new research skills and new understandings about oneself. Competing demands such as fluctuating membership and leadership, and the selection of a large initial project were identified as barriers to the Community of Practice Research. Implications for practice: As well as contributing to communities’ shared goals members should identify and make explicit their own learning goals to themselves, the community and their managers Community of practice meetings should include regular facilitated reflection about the learning that is occurring, the challenges and assumptions being made by the group, and the way forward A community of practice uses social processes to aid learning and collaboration across disciplines and organisations and therefore has potential to promote local culture change

Epidemic spreading on complex networks with overlapping and non-overlapping community structure

Science.gov (United States)

Shang, Jiaxing; Liu, Lianchen; Li, Xin; Xie, Feng; Wu, Cheng

2015-02-01

Many real-world networks exhibit community structure where vertices belong to one or more communities. Recent studies show that community structure plays an import role in epidemic spreading. In this paper, we investigate how the extent of overlap among communities affects epidemics. In order to experiment on the characteristic of overlapping communities, we propose a rewiring algorithm that can change the community structure from overlapping to non-overlapping while maintaining the degree distribution of the network. We simulate the Susceptible-Infected-Susceptible (SIS) epidemic process on synthetic scale-free networks and real-world networks by applying our rewiring algorithm. Experiments show that epidemics spread faster on networks with higher level of overlapping communities. Furthermore, overlapping communities' effect interacts with the average degree's effect. Our work further illustrates the important role of overlapping communities in the process of epidemic spreading.

Community Clustering Algorithm in Complex Networks Based on Microcommunity Fusion

Directory of Open Access Journals (Sweden)

Jin Qi

2015-01-01

Full Text Available With the further research on physical meaning and digital features of the community structure in complex networks in recent years, the improvement of effectiveness and efficiency of the community mining algorithms in complex networks has become an important subject in this area. This paper puts forward a concept of the microcommunity and gets final mining results of communities through fusing different microcommunities. This paper starts with the basic definition of the network community and applies Expansion to the microcommunity clustering which provides prerequisites for the microcommunity fusion. The proposed algorithm is more efficient and has higher solution quality compared with other similar algorithms through the analysis of test results based on network data set.

Immunization of networks with community structure

International Nuclear Information System (INIS)

Masuda, Naoki

2009-01-01

In this study, an efficient method to immunize modular networks (i.e. networks with community structure) is proposed. The immunization of networks aims at fragmenting networks into small parts with a small number of removed nodes. Its applications include prevention of epidemic spreading, protection against intentional attacks on networks, and conservation of ecosystems. Although preferential immunization of hubs is efficient, good immunization strategies for modular networks have not been established. On the basis of an immunization strategy based on eigenvector centrality, we develop an analytical framework for immunizing modular networks. To this end, we quantify the contribution of each node to the connectivity in a coarse-grained network among modules. We verify the effectiveness of the proposed method by applying it to model and real networks with modular structure.

Efficient inference of overlapping communities in complex networks

DEFF Research Database (Denmark)

Fruergaard, Bjarne Ørum; Herlau, Tue

2014-01-01

We discuss two views on extending existing methods for complex network modeling which we dub the communities first and the networks first view, respectively. Inspired by the networks first view that we attribute to White, Boorman, and Breiger (1976)[1], we formulate the multiple-networks stochastic...

A complex network based model for detecting isolated communities in water distribution networks

Science.gov (United States)

Sheng, Nan; Jia, Youwei; Xu, Zhao; Ho, Siu-Lau; Wai Kan, Chi

2013-12-01

Water distribution network (WDN) is a typical real-world complex network of major infrastructure that plays an important role in human's daily life. In this paper, we explore the formation of isolated communities in WDN based on complex network theory. A graph-algebraic model is proposed to effectively detect the potential communities due to pipeline failures. This model can properly illustrate the connectivity and evolution of WDN during different stages of contingency events, and identify the emerging isolated communities through spectral analysis on Laplacian matrix. A case study on a practical urban WDN in China is conducted, and the consistency between the simulation results and the historical data are reported to showcase the feasibility and effectiveness of the proposed model.

QSAR modelling using combined simple competitive learning networks and RBF neural networks.

Science.gov (United States)

Sheikhpour, R; Sarram, M A; Rezaeian, M; Sheikhpour, E

2018-04-01

The aim of this study was to propose a QSAR modelling approach based on the combination of simple competitive learning (SCL) networks with radial basis function (RBF) neural networks for predicting the biological activity of chemical compounds. The proposed QSAR method consisted of two phases. In the first phase, an SCL network was applied to determine the centres of an RBF neural network. In the second phase, the RBF neural network was used to predict the biological activity of various phenols and Rho kinase (ROCK) inhibitors. The predictive ability of the proposed QSAR models was evaluated and compared with other QSAR models using external validation. The results of this study showed that the proposed QSAR modelling approach leads to better performances than other models in predicting the biological activity of chemical compounds. This indicated the efficiency of simple competitive learning networks in determining the centres of RBF neural networks.

Distance Learning for Community Education

Science.gov (United States)

Cook, Anthony A.

2010-01-01

This article takes a look at the influence of technology on curriculum and teaching. It specifically examines the new wave of available technology and the opportunity for schools to make inroads into community outreach by engaging new, technological learning methods. The relationship among community education, public school relations, and distance…

A Social Network Perspective on Peer Supported Learning in MOOCs for Educators

Directory of Open Access Journals (Sweden)

Shaun Kellogg

2014-11-01

Full Text Available A recent phenomenon in the MOOC space has been the development of courses tailored to educators serving in K-12 settings. MOOCs, particularly as a form of educator professional development, face a number of challenges. Academics, as well as pundits from traditional and new media, have raised a number of concerns about MOOCs, including the lack of instructional and social supports. It is an assumption of this study that challenges arising form this problem of scale can be addressed by leveraging these massive numbers to develop robust online learning communities. This mixed-methods case study addresses critical gaps in the literature and issues of peer support in MOOCs through an examination of the characteristics, mechanisms, and outcomes of peer networks. Findings from this study demonstrate that even with technology as basic as a discussion forum, MOOCs can be leveraged to foster these networks and facilitate peer-supported learning. Although this study was limited to two unique cases along the wide spectrum of MOOCs, the methods applied provide other researchers with an approach for better understanding the dynamic process of peer supported learning in MOOCs.

Multi-modal Social Networks: A MRF Learning Approach

Science.gov (United States)

2016-06-20

Network forensics: random infection vs spreading epidemic , Proceedings of ACM Sigmetrics. 11-JUN-12, London, UK. : , TOTAL: 4 06/09/2016 Received Paper...Multi-modal Social Networks A MRF Learning Approach The work primarily focused on two lines of research. 1. We propose new greedy algorithms...Box 12211 Research Triangle Park, NC 27709-2211 social networks , learning and inference REPORT DOCUMENTATION PAGE 11. SPONSOR/MONITOR’S REPORT

Perceptions of School Principals on Participation in Professional Learning Communities as Job-Embedded Learning

Science.gov (United States)

Gaudioso, Jennifer A.

2017-01-01

Perceptions of School Principals on Participation in Professional Learning Communities as Job-Embedded Learning Jennifer Gaudioso Principal Professional Learning Communities (PPLCs) have emerged as a vehicle for professional development of principals, but there is little research on how principals experience PPLCs or how districts can support…

[Networks of experiences on community health as an information system in health promotion: lessons learned in Aragon (Spain)].

Science.gov (United States)

Gállego-Diéguez, Javier; Aliaga Traín, Pilar; Benedé Azagra, Carmen Belén; Bueno Franco, Manuel; Ferrer Gracia, Elisa; Ipiéns Sarrate, José Ramón; Muñoz Nadal, Pilar; Plumed Parrilla, Manuela; Vilches Urrutia, Begoña

2016-11-01

Networks of community health experiences promote interaction and knowledge management in health promotion among their participants. These networks integrate both professionals and social agents who work directly on the ground in small environments, with defined objectives and inclusion criteria and voluntary participation. In this article, networks in Aragon (Spain) are reviewed in order to analyse their role as an information system. The Health Promotion Projects Network of Aragon (Red Aragonesa de Proyectos de Promoción de la Salud, RAPPS) was launched in 1996 and currently includes 73 projects. The average duration of projects is 12.7 years. RAPPS interdisciplinary teams involve 701 people, of which 89.6% are professionals and 10.6% are social agents. The Aragon Health Promoting Schools Network (Red Aragonesa de Escuelas Promotoras de Salud, RAEPS) integrates 134 schools (24.9% of Aragon). The schools teams involve 829 teachers and members of the school community, students (35.2%), families (26.2%) and primary care health professionals (9.8%). Experiences Networks boost citizen participation, have an influence in changing social determinants and contribute to the formulation of plans and regional strategies. Networks can provide indicators for a health promotion information and monitoring system on: capacity building services in the territory, identifying assets and models of good practice, cross-sectoral and equity initiatives. Experiences Networks represent an opportunity to create a health promotion information system, systematising available information and establishing quality criteria for initiatives. Copyright © 2016 SESPAS. Publicado por Elsevier España, S.L.U. All rights reserved.

An Examination of the Impact of Learning Communities on Job

Science.gov (United States)

Wilmes, David M.

2012-01-01

The purpose of this study was to examine the relationship between learning community participation and job/major congruence. Previous research has demonstrated that learning communities are effective vehicles for promoting student and institutional outcomes. However, few studies have examined the impact of learning communities on alumni or career…

Engaging students in a community of learning: Renegotiating the learning environment.

Science.gov (United States)

Theobald, Karen A; Windsor, Carol A; Forster, Elizabeth M

2018-03-01

Promoting student engagement in a student led environment can be challenging. This article reports on the process of design, implementation and evaluation of a student led learning approach in a small group tutorial environment in a three year Bachelor of Nursing program at an Australian university. The research employed three phases of data collection. The first phase explored student perceptions of learning and engagement in tutorials. The results informed the development of a web based learning resource. Phase two centred on implementation of a community of learning approach where students were supported to lead tutorial learning with peers. The final phase constituted an evaluation of the new approach. Findings suggest that students have the capacity to lead and engage in a community of learning and to assume greater ownership and responsibility where scaffolding is provided. Nonetheless, an ongoing whole of course approach to pedagogical change would better support this form of teaching and learning innovation. Copyright © 2018 Elsevier Ltd. All rights reserved.

Machine Learning Topological Invariants with Neural Networks

Science.gov (United States)

Zhang, Pengfei; Shen, Huitao; Zhai, Hui

2018-02-01

In this Letter we supervisedly train neural networks to distinguish different topological phases in the context of topological band insulators. After training with Hamiltonians of one-dimensional insulators with chiral symmetry, the neural network can predict their topological winding numbers with nearly 100% accuracy, even for Hamiltonians with larger winding numbers that are not included in the training data. These results show a remarkable success that the neural network can capture the global and nonlinear topological features of quantum phases from local inputs. By opening up the neural network, we confirm that the network does learn the discrete version of the winding number formula. We also make a couple of remarks regarding the role of the symmetry and the opposite effect of regularization techniques when applying machine learning to physical systems.

Learning Analytics for Communities of Inquiry

Science.gov (United States)

Kovanovic, Vitomir; Gaševic, Dragan; Hatala, Marek

2014-01-01

This paper describes doctoral research that focuses on the development of a learning analytics framework for inquiry-based digital learning. Building on the Community of Inquiry model (CoI)--a foundation commonly used in the research and practice of digital learning and teaching--this research builds on the existing body of knowledge in two…

Ecological Networks and Community Attachment and Support Among Recently Resettled Refugees.

Science.gov (United States)

Soller, Brian; Goodkind, Jessica R; Greene, R Neil; Browning, Christopher R; Shantzek, Cece

2018-03-25

Interventions aimed at enhancing mental health are increasingly centered around promoting community attachment and support. However, few have examined and tested the specific ecological factors that give rise to these key community processes. Drawing from insights from the ecological network perspective, we tested whether spatial and social overlap in routine activity settings (e.g., work, school, childcare) with fellow ethnic community members is associated with individuals' attachment to their ethnic communities and access to social resources embedded in their communities. Data on routine activity locations drawn from the Refugee Well-Being Project (based in a city in the Southwestern United States) were used to reconstruct the ecological networks of recently resettled refugee communities, which were two-mode networks that comprise individuals and their routine activity locations. Results indicated that respondents' community attachment and support increased with their ecological network extensity-which taps the extent to which respondents share routine activity locations with other community members. Our study highlights a key ecological process that potentially enhances individuals' ethnic community attachment that extends beyond residential neighborhoods. © Society for Community Research and Action 2018.

Functionality for learning networks: lessons learned from social web applications

NARCIS (Netherlands)

Berlanga, Adriana; Sloep, Peter; Brouns, Francis; Van Rosmalen, Peter; Bitter-Rijpkema, Marlies; Koper, Rob

2007-01-01

Berlanga, A. J., Sloep, P., Brouns, F., Van Rosmalen, P., Bitter-Rijpkema, M., & Koper, R. (2007). Functionality for learning networks: lessons learned from social web applications. Proceedings of the ePortfolio 2007 Conference. October, 18-19, 2007, Maastricht, The Netherlands. [See also

On-line learning in radial basis functions networks

OpenAIRE

Freeman, Jason; Saad, David

1997-01-01

An analytic investigation of the average case learning and generalization properties of Radial Basis Function Networks (RBFs) is presented, utilising on-line gradient descent as the learning rule. The analytic method employed allows both the calculation of generalization error and the examination of the internal dynamics of the network. The generalization error and internal dynamics are then used to examine the role of the learning rate and the specialization of the hidden units, which gives ...

Exponential random graph models for networks with community structure.

Science.gov (United States)

Fronczak, Piotr; Fronczak, Agata; Bujok, Maksymilian

2013-09-01

Although the community structure organization is an important characteristic of real-world networks, most of the traditional network models fail to reproduce the feature. Therefore, the models are useless as benchmark graphs for testing community detection algorithms. They are also inadequate to predict various properties of real networks. With this paper we intend to fill the gap. We develop an exponential random graph approach to networks with community structure. To this end we mainly built upon the idea of blockmodels. We consider both the classical blockmodel and its degree-corrected counterpart and study many of their properties analytically. We show that in the degree-corrected blockmodel, node degrees display an interesting scaling property, which is reminiscent of what is observed in real-world fractal networks. A short description of Monte Carlo simulations of the models is also given in the hope of being useful to others working in the field.

Approximation methods for efficient learning of Bayesian networks

CERN Document Server

Riggelsen, C

2008-01-01

This publication offers and investigates efficient Monte Carlo simulation methods in order to realize a Bayesian approach to approximate learning of Bayesian networks from both complete and incomplete data. For large amounts of incomplete data when Monte Carlo methods are inefficient, approximations are implemented, such that learning remains feasible, albeit non-Bayesian. The topics discussed are: basic concepts about probabilities, graph theory and conditional independence; Bayesian network learning from data; Monte Carlo simulation techniques; and, the concept of incomplete data. In order to provide a coherent treatment of matters, thereby helping the reader to gain a thorough understanding of the whole concept of learning Bayesian networks from (in)complete data, this publication combines in a clarifying way all the issues presented in the papers with previously unpublished work.

Adaptive Learning in Weighted Network Games

NARCIS (Netherlands)

Bayer, Péter; Herings, P. Jean-Jacques; Peeters, Ronald; Thuijsman, Frank

2017-01-01

This paper studies adaptive learning in the class of weighted network games. This class of games includes applications like research and development within interlinked firms, crime within social networks, the economics of pollution, and defense expenditures within allied nations. We show that for

Community action research track: Community-based participatory research and service-learning experiences for medical students.

Science.gov (United States)

Gimpel, Nora; Kindratt, Tiffany; Dawson, Alvin; Pagels, Patti

2018-04-01

Community-based participatory research (CBPR) and service-learning are unique experiential approaches designed to train medical students how to provide individualized patient care from a population perspective. Medical schools in the US are required to provide support for service-learning and community projects. Despite this requirement, few medical schools offer structured service-learning. We developed the Community Action Research Track (CART) to integrate population medicine, health promotion/disease prevention and the social determinants of health into the medical school curriculum through CBPR and service-learning experiences. This article provides an overview of CART and reports the program impact based on students' participation, preliminary evaluations and accomplishments. CART is an optional 4‑year service-learning experience for medical students interested in community health. The curriculum includes a coordinated longitudinal program of electives, community service-learning and lecture-based instruction. From 2009-2015, 146 CART students participated. Interests in public health (93%), community service (73%), primary care (73%), CBPR (60%) and community medicine (60%) were the top reasons for enrolment. Significant improvements in mean knowledge were found when measuring the principles of CBPR, levels of prevention, determining health literacy and patient communication strategies (all p's Projects were disseminated by at least 65 posters and four oral presentations at local, national and international professional meetings. Six manuscripts were published in peer-reviewed journals. CART is an innovative curriculum for training future physicians to be community-responsive physicians. CART can be replicated by other medical schools interested in offering a longitudinal CBPR and service-learning track in an urban metropolitan setting.

The Sea Floor: A Living Learning Residential Community

Science.gov (United States)

Guentzel, J. L.; Rosch, E.; Stoughton, M. A.; Bowyer, R.; Mortensen, K.; Smith, M.

2016-02-01

Living learning communities are collaborations between university housing and academic departments designed to enhance the overall student experience by integrating classroom/laboratory learning, student life and extracurricular activities. At Coastal Carolina University, the residential community associated with the Marine Science program is known as the Sea Floor. Students selected to become members of the Sea Floor remain "in residence" for two consecutive semesters. These students are first-time freshman that share a common course connection. This course is usually Introduction to Marine Science (MSCI 111) or MSCI 399s, which are one credit field/laboratory centered internships. The common course connection is designed so residents can establish and maintain an educational dialog with their peers. Activities designed to enhance the students' networking skills and educational and social development skills include monthly lunches with marine science faculty and dinner seminars with guest speakers from academia, industry and government. Additionally, each semester several activities outside the classroom are planned so that students can more frequently interact with themselves and their faculty and staff partners. These activities include field trips to regional aquariums, local boat trips that include water sample collection and analysis, and an alternative spring break trip to the Florida Keys to study the marine environment firsthand. The resident advisor that supervises the Sea Floor is usually a sophomore or junior marine science major. This provides the residents with daily communication and mentoring from a marine science major that is familiar with the marine science program and residence life. Assessment activities include: a university housing community living survey, student interest housing focus groups, fall to spring and fall to fall retention, and evaluation of program advisors and program activities.

Learning by stimulation avoidance: A principle to control spiking neural networks dynamics.

Science.gov (United States)

Sinapayen, Lana; Masumori, Atsushi; Ikegami, Takashi

2017-01-01

Learning based on networks of real neurons, and learning based on biologically inspired models of neural networks, have yet to find general learning rules leading to widespread applications. In this paper, we argue for the existence of a principle allowing to steer the dynamics of a biologically inspired neural network. Using carefully timed external stimulation, the network can be driven towards a desired dynamical state. We term this principle "Learning by Stimulation Avoidance" (LSA). We demonstrate through simulation that the minimal sufficient conditions leading to LSA in artificial networks are also sufficient to reproduce learning results similar to those obtained in biological neurons by Shahaf and Marom, and in addition explains synaptic pruning. We examined the underlying mechanism by simulating a small network of 3 neurons, then scaled it up to a hundred neurons. We show that LSA has a higher explanatory power than existing hypotheses about the response of biological neural networks to external simulation, and can be used as a learning rule for an embodied application: learning of wall avoidance by a simulated robot. In other works, reinforcement learning with spiking networks can be obtained through global reward signals akin simulating the dopamine system; we believe that this is the first project demonstrating sensory-motor learning with random spiking networks through Hebbian learning relying on environmental conditions without a separate reward system.

Networks of Learning

Science.gov (United States)

Bettencourt, Luis; Kaiser, David

2004-03-01

Based on an a historically documented example of scientific discovery - Feynman diagrams as the main calculational tool of theoretical high energy Physics - we map the time evolution of the social network of early adopters through in the US, UK, Japan and the USSR. The spread of the technique for total number of users in each region is then modelled in terms of epidemic models, highlighting parallel and divergent aspects of this analogy. We also show that transient social arrangements develop as the idea is introduced and learned, which later disappear as the technique becomes common knowledge. Such early transient is characterized by abnormally low connectivity distribution powers and by high clustering. This interesting early non-equilibrium stage of network evolution is captured by a new dynamical model for network evolution, which coincides in its long time limit with familiar preferential aggregation dynamics.

The CIRTL Network: A Professional Development Network for Future STEM Faculty

Science.gov (United States)

Herbert, B. E.

2011-12-01

The Center for the Integration of Research, Teaching, and Learning (CIRTL) is an NSF Center for Learning and Teaching in higher education using the professional development of graduate students and post-doctoral scholars as the leverage point to develop a national STEM faculty committed to implementing and advancing effective teaching practices for diverse student audiences as part of successful professional careers. The goal of CIRTL is to improve the STEM learning of all students at every college and university, and thereby to increase the diversity in STEM fields and the STEM literacy of the nation. The CIRTL network seeks to support change at a number of levels to support its goals: individual, classroom, institutional, and national. To bring about change, which is never easy, the CIRTL network has developed a conceptual model or change model that is thought to support the program objectives. Three central concepts, Teaching-as-Research, Learning Communities, and Learning-through-Diversity, underlie the design of all CIRTL activities. STEM faculty use research methods to systematically and reflectively improve learning outcomes. This work is done within a community of shared learning and discovery, and explicitly recognizes that effective teaching capitalizes on the rich array of experiences, backgrounds, and skills among the students and instructors to enhance the learning of all. This model is being refined and tested through a networked-design experiment, where the model is tested in diverse settings. Established in fall 2006, the CIRTL Network comprises the University of Colorado at Boulder (CU), Howard University, Michigan State University, Texas A&M University, Vanderbilt University, and the University of Wisconsin-Madison. The diversity of these institutions is by design: private/public; large/moderate size; majority-/minority-serving; geographic location. This talk will describe the theoretical constructs and efficacy of Teaching-as Research as a

Learning Orthographic Structure With Sequential Generative Neural Networks.

Science.gov (United States)

Testolin, Alberto; Stoianov, Ivilin; Sperduti, Alessandro; Zorzi, Marco

2016-04-01

Learning the structure of event sequences is a ubiquitous problem in cognition and particularly in language. One possible solution is to learn a probabilistic generative model of sequences that allows making predictions about upcoming events. Though appealing from a neurobiological standpoint, this approach is typically not pursued in connectionist modeling. Here, we investigated a sequential version of the restricted Boltzmann machine (RBM), a stochastic recurrent neural network that extracts high-order structure from sensory data through unsupervised generative learning and can encode contextual information in the form of internal, distributed representations. We assessed whether this type of network can extract the orthographic structure of English monosyllables by learning a generative model of the letter sequences forming a word training corpus. We show that the network learned an accurate probabilistic model of English graphotactics, which can be used to make predictions about the letter following a given context as well as to autonomously generate high-quality pseudowords. The model was compared to an extended version of simple recurrent networks, augmented with a stochastic process that allows autonomous generation of sequences, and to non-connectionist probabilistic models (n-grams and hidden Markov models). We conclude that sequential RBMs and stochastic simple recurrent networks are promising candidates for modeling cognition in the temporal domain. Copyright © 2015 Cognitive Science Society, Inc.

21st Century Community Learning Centers: Providing Afterschool and Summer Learning Support to Communities Nationwide

Science.gov (United States)

Afterschool Alliance, 2014

2014-01-01

The 21st Century Community Learning Centers (21st CCLC) initiative is the only federal funding source dedicated exclusively to before-school, afterschool, and summer learning programs. Each state education agency receives funds based on its share of Title I funding for low-income students at high-poverty, low performing schools. Funds are also…

Evolution of individual versus social learning on social networks.

Science.gov (United States)

Tamura, Kohei; Kobayashi, Yutaka; Ihara, Yasuo

2015-03-06

A number of studies have investigated the roles played by individual and social learning in cultural phenomena and the relative advantages of the two learning strategies in variable environments. Because social learning involves the acquisition of behaviours from others, its utility depends on the availability of 'cultural models' exhibiting adaptive behaviours. This indicates that social networks play an essential role in the evolution of learning. However, possible effects of social structure on the evolution of learning have not been fully explored. Here, we develop a mathematical model to explore the evolutionary dynamics of learning strategies on social networks. We first derive the condition under which social learners (SLs) are selectively favoured over individual learners in a broad range of social network. We then obtain an analytical approximation of the long-term average frequency of SLs in homogeneous networks, from which we specify the condition, in terms of three relatedness measures, for social structure to facilitate the long-term evolution of social learning. Finally, we evaluate our approximation by Monte Carlo simulations in complete graphs, regular random graphs and scale-free networks. We formally show that whether social structure favours the evolution of social learning is determined by the relative magnitudes of two effects of social structure: localization in competition, by which competition between learning strategies is evaded, and localization in cultural transmission, which slows down the spread of adaptive traits. In addition, our estimates of the relatedness measures suggest that social structure disfavours the evolution of social learning when selection is weak. © 2015 The Author(s) Published by the Royal Society. All rights reserved.

Biologically-inspired Learning in Pulsed Neural Networks

DEFF Research Database (Denmark)

Lehmann, Torsten; Woodburn, Robin

1999-01-01

Self-learning chips to implement many popular ANN (artificial neural network) algorithms are very difficult to design. We explain why this is so and say what lessons previous work teaches us in the design of self-learning systems. We offer a contribution to the `biologically-inspired' approach......, explaining what we mean by this term and providing an example of a robust, self-learning design that can solve simple classical-conditioning tasks. We give details of the design of individual circuits to perform component functions, which can then be combined into a network to solve the task. We argue...

Dynamic robustness of knowledge collaboration network of open source product development community

Science.gov (United States)

Zhou, Hong-Li; Zhang, Xiao-Dong

2018-01-01

As an emergent innovative design style, open source product development communities are characterized by a self-organizing, mass collaborative, networked structure. The robustness of the community is critical to its performance. Using the complex network modeling method, the knowledge collaboration network of the community is formulated, and the robustness of the network is systematically and dynamically studied. The characteristics of the network along the development period determine that its robustness should be studied from three time stages: the start-up, development and mature stages of the network. Five kinds of user-loss pattern are designed, to assess the network's robustness under different situations in each of these three time stages. Two indexes - the largest connected component and the network efficiency - are used to evaluate the robustness of the community. The proposed approach is applied in an existing open source car design community. The results indicate that the knowledge collaboration networks show different levels of robustness in different stages and different user loss patterns. Such analysis can be applied to provide protection strategies for the key users involved in knowledge dissemination and knowledge contribution at different stages of the network, thereby promoting the sustainable and stable development of the open source community.

Learning oncogenetic networks by reducing to mixed integer linear programming.

Science.gov (United States)

Shahrabi Farahani, Hossein; Lagergren, Jens

2013-01-01

Cancer can be a result of accumulation of different types of genetic mutations such as copy number aberrations. The data from tumors are cross-sectional and do not contain the temporal order of the genetic events. Finding the order in which the genetic events have occurred and progression pathways are of vital importance in understanding the disease. In order to model cancer progression, we propose Progression Networks, a special case of Bayesian networks, that are tailored to model disease progression. Progression networks have similarities with Conjunctive Bayesian Networks (CBNs) [1],a variation of Bayesian networks also proposed for modeling disease progression. We also describe a learning algorithm for learning Bayesian networks in general and progression networks in particular. We reduce the hard problem of learning the Bayesian and progression networks to Mixed Integer Linear Programming (MILP). MILP is a Non-deterministic Polynomial-time complete (NP-complete) problem for which very good heuristics exists. We tested our algorithm on synthetic and real cytogenetic data from renal cell carcinoma. We also compared our learned progression networks with the networks proposed in earlier publications. The software is available on the website https://bitbucket.org/farahani/diprog.

Learning for Life

Science.gov (United States)

Robotham, Dan

2011-01-01

People working in the field of education know well the positive effects adult and community learning can have on mental health and wellbeing. Participating in adult and community learning can help to widen social networks and improve life and employment chances; it makes for better general health; and can strengthen the learner's self-confidence,…

Modeling information diffusion in time-varying community networks

Science.gov (United States)

Cui, Xuelian; Zhao, Narisa

2017-12-01

Social networks are rarely static, and they typically have time-varying network topologies. A great number of studies have modeled temporal networks and explored social contagion processes within these models; however, few of these studies have considered community structure variations. In this paper, we present a study of how the time-varying property of a modular structure influences the information dissemination. First, we propose a continuous-time Markov model of information diffusion where two parameters, mobility rate and community attractiveness, are introduced to address the time-varying nature of the community structure. The basic reproduction number is derived, and the accuracy of this model is evaluated by comparing the simulation and theoretical results. Furthermore, numerical results illustrate that generally both the mobility rate and community attractiveness significantly promote the information diffusion process, especially in the initial outbreak stage. Moreover, the strength of this promotion effect is much stronger when the modularity is higher. Counterintuitively, it is found that when all communities have the same attractiveness, social mobility no longer accelerates the diffusion process. In addition, we show that the local spreading in the advantage group has been greatly enhanced due to the agglomeration effect caused by the social mobility and community attractiveness difference, which thus increases the global spreading.

Network analysis as a tool for community capacity measurement and assessing partnerships between community-based organizations in Korea.

Science.gov (United States)

Jung, Minsoo

2012-01-01

The community partnership is a foundation laid by the local community that has been historically and geographically formed to develop itself. This article, an exploratory community network survey for capacity building, assessed collaborations among community-based organizations (CBOs) in the S-district, Republic of Korea, and evaluated methods for the reconstruction of a resident-governing healthy network. Using CBOs' evaluation questionnaire, the author surveyed 83 CBOs that were collected by snowball sampling. The CBOs in the S-district had formed community networks based on vocational associations established in the 1980s and the 1990s. The entire network evidenced a cooperative partnership, in which women's organizations and civic groups carried out essential functions. In the capacity-building process through CBOs, community collaboration can be naturally cultivated, and health promotion programs to improve the residents' health will tend to be more systematic than the current approach and yield higher compliance and practice rates. Thus, it will be necessary to construct an effective partnership of community networks by reorganizing existing exclusive relations.

Asymmetric intimacy and algorithm for detecting communities in bipartite networks

Science.gov (United States)

Wang, Xingyuan; Qin, Xiaomeng

2016-11-01

In this paper, an algorithm to choose a good partition in bipartite networks has been proposed. Bipartite networks have more theoretical significance and broader prospect of application. In view of distinctive structure of bipartite networks, in our method, two parameters are defined to show the relationships between the same type nodes and heterogeneous nodes respectively. Moreover, our algorithm employs a new method of finding and expanding the core communities in bipartite networks. Two kinds of nodes are handled separately and merged, and then the sub-communities are obtained. After that, objective communities will be found according to the merging rule. The proposed algorithm has been simulated in real-world networks and artificial networks, and the result verifies the accuracy and reliability of the parameters on intimacy for our algorithm. Eventually, comparisons with similar algorithms depict that the proposed algorithm has better performance.

Community Size Effects on Epidemic Spreading in Multiplex Social Networks.

Directory of Open Access Journals (Sweden)

Ting Liu

Full Text Available The dynamical process of epidemic spreading has drawn much attention of the complex network community. In the network paradigm, diseases spread from one person to another through the social ties amongst the population. There are a variety of factors that govern the processes of disease spreading on the networks. A common but not negligible factor is people's reaction to the outbreak of epidemics. Such reaction can be related information dissemination or self-protection. In this work, we explore the interactions between disease spreading and population response in terms of information diffusion and individuals' alertness. We model the system by mapping multiplex networks into two-layer networks and incorporating individuals' risk awareness, on the assumption that their response to the disease spreading depends on the size of the community they belong to. By comparing the final incidence of diseases in multiplex networks, we find that there is considerable mitigation of diseases spreading for full phase of spreading speed when individuals' protection responses are introduced. Interestingly, the degree of community overlap between the two layers is found to be critical factor that affects the final incidence. We also analyze the consequences of the epidemic incidence in communities with different sizes and the impacts of community overlap between two layers. Specifically, as the diseases information makes individuals alert and take measures to prevent the diseases, the effective protection is more striking in small community. These phenomena can be explained by the multiplexity of the networked system and the competition between two spreading processes.

Community Size Effects on Epidemic Spreading in Multiplex Social Networks.

Science.gov (United States)

Liu, Ting; Li, Ping; Chen, Yan; Zhang, Jie

2016-01-01

The dynamical process of epidemic spreading has drawn much attention of the complex network community. In the network paradigm, diseases spread from one person to another through the social ties amongst the population. There are a variety of factors that govern the processes of disease spreading on the networks. A common but not negligible factor is people's reaction to the outbreak of epidemics. Such reaction can be related information dissemination or self-protection. In this work, we explore the interactions between disease spreading and population response in terms of information diffusion and individuals' alertness. We model the system by mapping multiplex networks into two-layer networks and incorporating individuals' risk awareness, on the assumption that their response to the disease spreading depends on the size of the community they belong to. By comparing the final incidence of diseases in multiplex networks, we find that there is considerable mitigation of diseases spreading for full phase of spreading speed when individuals' protection responses are introduced. Interestingly, the degree of community overlap between the two layers is found to be critical factor that affects the final incidence. We also analyze the consequences of the epidemic incidence in communities with different sizes and the impacts of community overlap between two layers. Specifically, as the diseases information makes individuals alert and take measures to prevent the diseases, the effective protection is more striking in small community. These phenomena can be explained by the multiplexity of the networked system and the competition between two spreading processes.

Epidemic spreading in weighted scale-free networks with community structure

International Nuclear Information System (INIS)

Chu, Xiangwei; Guan, Jihong; Zhang, Zhongzhi; Zhou, Shuigeng

2009-01-01

Many empirical studies reveal that the weights and community structure are ubiquitous in various natural and artificial networks. In this paper, based on the SI disease model, we investigate the epidemic spreading in weighted scale-free networks with community structure. Two exponents, α and β, are introduced to weight the internal edges and external edges, respectively; and a tunable probability parameter q is also introduced to adjust the strength of community structure. We find the external weighting exponent β plays a much more important role in slackening the epidemic spreading and reducing the danger brought by the epidemic than the internal weighting exponent α. Moreover, a novel result we find is that the strong community structure is no longer helpful for slackening the danger brought by the epidemic in the weighted cases. In addition, we show the hierarchical dynamics of the epidemic spreading in the weighted scale-free networks with communities which is also displayed in the famous BA scale-free networks

Identifying and characterizing key nodes among communities based on electrical-circuit networks.

Science.gov (United States)

Zhu, Fenghui; Wang, Wenxu; Di, Zengru; Fan, Ying

2014-01-01

Complex networks with community structures are ubiquitous in the real world. Despite many approaches developed for detecting communities, we continue to lack tools for identifying overlapping and bridging nodes that play crucial roles in the interactions and communications among communities in complex networks. Here we develop an algorithm based on the local flow conservation to effectively and efficiently identify and distinguish the two types of nodes. Our method is applicable in both undirected and directed networks without a priori knowledge of the community structure. Our method bypasses the extremely challenging problem of partitioning communities in the presence of overlapping nodes that may belong to multiple communities. Due to the fact that overlapping and bridging nodes are of paramount importance in maintaining the function of many social and biological networks, our tools open new avenues towards understanding and controlling real complex networks with communities accompanied with the key nodes.

Community garden: A bridging program between formal and informal learning

Directory of Open Access Journals (Sweden)

Ranjan Datta

2016-12-01

Full Text Available Community garden activities can play a significant role in bridging formal and informal learning, particularly in urban children’s science and environmental education. It promotes relational methods of learning, discussing, and practicing that will integrate food security, social interactions, community development, environmental activism, and cultural integration. Throughout the last five years of my community garden activities, I have learned that community garden-based practices adhere to particular forms of agency: embracing diversity, sharing power, and trust building as a part of everyday learning. My auto-ethnographic study provides valuable insights for environmental educators whose goals include, incorporating ethnic diversity as well as engaging children in research, ultimately leading to community action.

THE PAN AFRICAN E-NETWORK PROJECT: A New Learning Culture

Directory of Open Access Journals (Sweden)

Nundoo-Ghoorah SUNITI

2012-07-01

Full Text Available This paper sets out to explore the paradigm shift in learning culture brought about by the advent of online learning in the mostly print-based ODL system at the Mauritius College of the Air (MCA. It delves into the perceptions of learners and MCA staff involved in a range of undergraduate to Master’s programmes forming part of the Pan African e- Network Project that wires 23 African countries with top-ranking Indian universities through synchronous and interactive state of the art technology. Learners across five of the disciplines offered through tele-learning and a team of MCA staff participating in programme delivery were surveyed through questionnaires and interviews to collect both quantitative and qualitative data. For most of the respondents this new learning ethos has induced an acculturation process requiring radical reconceptualisation of prior notions about teaching/learning. MCA staff, too, have had to learn conducive behavior patterns to consolidate existing support services. Survival in this new learning environment where the tutor is a remote on-screen entity, where e-books replace printed material, where connectivity can be a daily struggle, demands another mindset, another set of values enabling learning and fine-tuned ICT skills. Socialisation with tutors and fellow learners is possible through links in Facebook and Twitter. However, learners still tend to feel somewhat isolated. It is proposed that an e-platform be set up to link up learners as a mutually supportive learning community engaged in the construction of knowledge.

Effective Strategies for Sustaining Professional Learning Communities

Science.gov (United States)

Bennett, Patricia R.

2010-01-01

Professional Learning Communities (PLCs), in which educators work collaboratively to improve learning for students, need effective strategies to sustain them. PLCs promote continuous improvement in student learning and build academic success with increased teacher expertise. Grounded in organizational systems theory, participative leadership…

Guidelines for Lifelong Education Management to Mobilize Learning Community

Science.gov (United States)

Charungkaittikul, Suwithida

2018-01-01

This article is a study of the guidelines for lifelong education management to mobilize learning communities in the social-cultural context of Thailand is intended to 1) analyze and synthesize the management of lifelong learning to mobilize learning community in the social-cultural context of Thailand; and 2) propose guidelines for lifelong…

The Mobile Learning Network: Getting Serious about Games Technologies for Learning

Science.gov (United States)

Petley, Rebecca; Parker, Guy; Attewell, Jill

2011-01-01

The Mobile Learning Network currently in its third year, is a unique collaborative initiative encouraging and enabling the introduction of mobile learning in English post-14 education. The programme, funded jointly by the Learning and Skills Council and participating colleges and schools and supported by LSN has involved nearly 40,000 learners and…

Learning drifting concepts with neural networks

NARCIS (Netherlands)

Biehl, Michael; Schwarze, Holm

1993-01-01

The learning of time-dependent concepts with a neural network is studied analytically and numerically. The linearly separable target rule is represented by an N-vector, whose time dependence is modelled by a random or deterministic drift process. A single-layer network is trained online using

Social Network and Content Analysis of the North American Carbon Program as a Scientific Community of Practice

Science.gov (United States)

Brown, Molly E.; Ihli, Monica; Hendrick, Oscar; Delgado-Arias, Sabrina; Escobar, Vanessa M.; Griffith, Peter

2015-01-01

The North American Carbon Program (NACP) was formed to further the scientific understanding of sources, sinks, and stocks of carbon in Earth's environment. Carbon cycle science integrates multidisciplinary research, providing decision-support information for managing climate and carbon-related change across multiple sectors of society. This investigation uses the conceptual framework of com-munities of practice (CoP) to explore the role that the NACP has played in connecting researchers into a carbon cycle knowledge network, and in enabling them to conduct physical science that includes ideas from social science. A CoP describes the communities formed when people consistently engage in shared communication and activities toward a common passion or learning goal. We apply the CoP model by using keyword analysis of abstracts from scientific publications to analyze the research outputs of the NACP in terms of its knowledge domain. We also construct a co-authorship network from the publications of core NACP members, describe the structure and social pathways within the community. Results of the content analysis indicate that the NACP community of practice has substantially expanded its research on human and social impacts on the carbon cycle, contributing to a better understanding of how human and physical processes interact with one another. Results of the co-authorship social network analysis demonstrate that the NACP has formed a tightly connected community with many social pathways through which knowledge may flow, and that it has also expanded its network of institutions involved in carbon cycle research over the past seven years.

Learning organisations

Directory of Open Access Journals (Sweden)

Sabina Jelenc Krašovec

2000-12-01

Full Text Available A vast array of economical, social, political, cultural and other factors influences the transformed role of learning and education in the society, as well as the functioning of local community and its social and communication patterns. The influences which are manifested as global problems can only be successfully solved on the level of local community. Analogously with the society in general, there is a great need of transforming a local community into a learning, flexible and interconnected environment which takes into account different interests, wishes and needs regarding learning and being active. The fundamental answer to changes is the strategy of lifelong learning and education which requires reorganisation of all walks of life (work, free time, family, mass media, culture, sport, education and transforming of organisations into learning organisations. With learning society based on networks of knowledge individuals are turning into learning individuals, and organisations into learning organisations; people who learn take the responsibility of their progress, learning denotes partnership among learning people, teachers, parents, employers and local community, so that they work together to achieve better results.

Social network analysis community detection and evolution

CERN Document Server

Missaoui, Rokia

2015-01-01

This book is devoted to recent progress in social network analysis with a high focus on community detection and evolution. The eleven chapters cover the identification of cohesive groups, core components and key players either in static or dynamic networks of different kinds and levels of heterogeneity. Other important topics in social network analysis such as influential detection and maximization, information propagation, user behavior analysis, as well as network modeling and visualization are also presented. Many studies are validated through real social networks such as Twitter. This edit

Engaging Karen refugee students in science learning through a cross-cultural learning community

Science.gov (United States)

Harper, Susan G.

2017-02-01

This research explored how Karen (first-generation refugees from Burma) elementary students engaged with the Next Generation Science Standards (NGSS) practice of constructing scientific explanations based on evidence within the context of a cross-cultural learning community. In this action research, the researcher and a Karen parent served as co-teachers for fourth- and fifth-grade Karen and non-Karen students in a science and culture after-school programme in a public elementary school in the rural southeastern United States. Photovoice provided a critical platform for students to create their own cultural discourses for the learning community. The theoretical framework of critical pedagogy of place provided a way for the learning community to decolonise and re-inhabit the learning spaces with knowledge they co-constructed. Narrative analysis of video transcripts of the after-school programme, ethnographic interviews, and focus group discussions from Photovoice revealed a pattern of emerging agency by Karen students in the scientific practice of constructing scientific explanations based on evidence and in Karen language lessons. This evidence suggests that science learning embedded within a cross-cultural learning community can empower refugee students to construct their own hybrid cultural knowledge and leverage that knowledge to engage in a meaningful way with the epistemology of science.

Fuzzy comprehensive evaluation model of interuniversity collaborative learning based on network

Science.gov (United States)

Wenhui, Ma; Yu, Wang

2017-06-01

Learning evaluation is an effective method, which plays an important role in the network education evaluation system. But most of the current network learning evaluation methods still use traditional university education evaluation system, which do not take into account of web-based learning characteristics, and they are difficult to fit the rapid development of interuniversity collaborative learning based on network. Fuzzy comprehensive evaluation method is used to evaluate interuniversity collaborative learning based on the combination of fuzzy theory and analytic hierarchy process. Analytic hierarchy process is used to determine the weight of evaluation factors of each layer and to carry out the consistency check. According to the fuzzy comprehensive evaluation method, we establish interuniversity collaborative learning evaluation mathematical model. The proposed scheme provides a new thought for interuniversity collaborative learning evaluation based on network.

Impulsive Cluster Synchronization in Community Network with Nonidentical Nodes

International Nuclear Information System (INIS)

Deng Liping; Wu Zhaoyan

2012-01-01

In this paper, cluster synchronization in community network with nonidentical nodes and impulsive effects is investigated. Community networks with two kinds of topological structure are investigated. Positive weighted network is considered first and external pinning controllers are designed for achieving cluster synchronization. Cooperative and competitive network under some assumptions is investigated as well and can achieve cluster synchronization with only impulsive controllers. Based on the stability analysis of impulsive differential equation and the Lyapunov stability theory, several simple and useful synchronization criteria are derived. Finally, numerical simulations are provided to verify the effectiveness of the derived results.

Brand communities embedded in social networks ?

OpenAIRE

Zaglia, Melanie E.

2013-01-01

Brand communities represent highly valuable marketing, innovation management, and customer relationship management tools. However, applying successful marketing strategies today, and in the future, also means exploring and seizing the unprecedented opportunities of social network environments. This study combines these two social phenomena which have largely been researched separately, and aims to investigate the existence, functionality and different types of brand communities within social ...

Maximum entropy methods for extracting the learned features of deep neural networks.

Science.gov (United States)

Finnegan, Alex; Song, Jun S

2017-10-01

New architectures of multilayer artificial neural networks and new methods for training them are rapidly revolutionizing the application of machine learning in diverse fields, including business, social science, physical sciences, and biology. Interpreting deep neural networks, however, currently remains elusive, and a critical challenge lies in understanding which meaningful features a network is actually learning. We present a general method for interpreting deep neural networks and extracting network-learned features from input data. We describe our algorithm in the context of biological sequence analysis. Our approach, based on ideas from statistical physics, samples from the maximum entropy distribution over possible sequences, anchored at an input sequence and subject to constraints implied by the empirical function learned by a network. Using our framework, we demonstrate that local transcription factor binding motifs can be identified from a network trained on ChIP-seq data and that nucleosome positioning signals are indeed learned by a network trained on chemical cleavage nucleosome maps. Imposing a further constraint on the maximum entropy distribution also allows us to probe whether a network is learning global sequence features, such as the high GC content in nucleosome-rich regions. This work thus provides valuable mathematical tools for interpreting and extracting learned features from feed-forward neural networks.

Addressing cancer disparities via community network mobilization and intersectoral partnerships: a social network analysis.

Directory of Open Access Journals (Sweden)

Shoba Ramanadhan

Full Text Available Community mobilization and collaboration among diverse partners are vital components of the effort to reduce and eliminate cancer disparities in the United States. We studied the development and impact of intersectoral connections among the members of the Massachusetts Community Network for Cancer Education, Research, and Training (MassCONECT. As one of the Community Network Program sites funded by the National Cancer Institute, this infrastructure-building initiative utilized principles of Community-based Participatory Research (CBPR to unite community coalitions, researchers, policymakers, and other important stakeholders to address cancer disparities in three Massachusetts communities: Boston, Lawrence, and Worcester. We conducted a cross-sectional, sociometric network analysis four years after the network was formed. A total of 38 of 55 members participated in the study (69% response rate. Over four years of collaboration, the number of intersectoral connections reported by members (intersectoral out-degree increased, as did the extent to which such connections were reported reciprocally (intersectoral reciprocity. We assessed relationships between these markers of intersectoral collaboration and three intermediate outcomes in the effort to reduce and eliminate cancer disparities: delivery of community activities, policy engagement, and grants/publications. We found a positive and statistically significant relationship between intersectoral out-degree and community activities and policy engagement (the relationship was borderline significant for grants/publications. We found a positive and statistically significant relationship between intersectoral reciprocity and community activities and grants/publications (the relationship was borderline significant for policy engagement. The study suggests that intersectoral connections may be important drivers of diverse intermediate outcomes in the effort to reduce and eliminate cancer disparities

An Efficient Hierarchy Algorithm for Community Detection in Complex Networks

Directory of Open Access Journals (Sweden)

Lili Zhang

2014-01-01

Full Text Available Community structure is one of the most fundamental and important topology characteristics of complex networks. The research on community structure has wide applications and is very important for analyzing the topology structure, understanding the functions, finding the hidden properties, and forecasting the time-varying of the networks. This paper analyzes some related algorithms and proposes a new algorithm—CN agglomerative algorithm based on graph theory and the local connectedness of network to find communities in network. We show this algorithm is distributed and polynomial; meanwhile the simulations show it is accurate and fine-grained. Furthermore, we modify this algorithm to get one modified CN algorithm and apply it to dynamic complex networks, and the simulations also verify that the modified CN algorithm has high accuracy too.

A general picture of the learning communities: characteristics, similarities and differences.

NARCIS (Netherlands)

Verkleij, K.A.M.; Francke, A.L.; Voordouw, I.; Albers, M.; Gobbens, R.J.J.

2016-01-01

Background: Because learning communities of community care nurses and nursing lectures are a new phenomenon, it is of interest to evaluate en monitor the learning communities. the Netherlands Institute for Health Services Research, NIVEL, was commissioned to monitor the realization of the learning

Complex brain networks: From topological communities to clustered

Indian Academy of Sciences (India)

Complex brain networks: From topological communities to clustered dynamics ... Recent research has revealed a rich and complicated network topology in the cortical connectivity of mammalian brains. ... Pramana – Journal of Physics | News.

Social Learning Networks: Build Mobile Learning Networks Based on Collaborative Services

Science.gov (United States)

Huang, Jeff J. S.; Yang, Stephen J. H.; Huang, Yueh-Min; Hsiao, Indy Y. T.

2010-01-01

Recently, the rising of Web 2.0 has made online community gradually become popular, like Facebook, blog, etc. As a result, the online knowledge sharing network formed by interpersonal interaction is now a major character of Web 2.0, and therefore, by this trend, we try to build up a collaborative service mechanism and further set up an analysis…

A density-based clustering model for community detection in complex networks

Science.gov (United States)

Zhao, Xiang; Li, Yantao; Qu, Zehui

2018-04-01

Network clustering (or graph partitioning) is an important technique for uncovering the underlying community structures in complex networks, which has been widely applied in various fields including astronomy, bioinformatics, sociology, and bibliometric. In this paper, we propose a density-based clustering model for community detection in complex networks (DCCN). The key idea is to find group centers with a higher density than their neighbors and a relatively large integrated-distance from nodes with higher density. The experimental results indicate that our approach is efficient and effective for community detection of complex networks.

Mentoring: A Natural Role for Learning Community Faculty

Science.gov (United States)

Hessenauer, Sarah L.; Law, Kristi

2017-01-01

The purpose of this article is to highlight mentoring as an important piece of leading a learning community. The authors will share a definition of mentoring which is applicable to the learning community experience. Characteristics of mentoring will be described, including types of mentoring and mentor-mentee relationships. The authors will apply…

Detecting and evaluating communities in complex human and biological networks

Science.gov (United States)

Morrison, Greg; Mahadevan, L.

2012-02-01

We develop a simple method for detecting the community structure in a network can by utilizing a measure of closeness between nodes. This approach readily leads to a method of coarse graining the network, which allows the detection of the natural hierarchy (or hierarchies) of community structure without appealing to an unknown resolution parameter. The closeness measure can also be used to evaluate the robustness of an individual node's assignment to its community (rather than evaluating only the quality of the global structure). Each of these methods in community detection and evaluation are illustrated using a variety of real world networks of either biological or sociological importance and illustrate the power and flexibility of the approach.