Social Network Analysis
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Social Network Analysis
Social Network Analysis - Un espacio para compartir herramietas, experiencias, investigaciones hacer de SNA y su aplicabilidad.
Curated by Pablo Torres
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Modularity and community structure in networks


Via luiy
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luiy's curator insight, May 10, 2013 9:25 AM

Many networks of interest in the sciences, including a variety of social and biological networks, are found to divide naturally into communities or modules. The problem of detecting and characterizing this community structure has attracted considerable recent attention. One of the most sensitive detection methods is optimization of the quality function known as “modularity” over the possible divisions of a network, but direct application of this method using, for instance, simulated annealing is computationally costly. Here we show that the modularity can be reformulated in terms of the eigenvectors of a new characteristic matrix for the network, which we call the modularity matrix, and that this reformulation leads to a spectral algorithm for community detection that returns results of better quality than competing methods in noticeably shorter running times. We demonstrate the algorithm with applications to several network data sets.

 

 

Example applications


In practice, the algorithm developed here gives excellent results. For a quantitative comparison between our algorithm and others we follow Duch and Arenas [19] and compare values of the modularity for a variety of
networks drawn from the literature. Results are shown in Table I for six different networks—the exact same six as used by Duch and Arenas. We compare modularity figures against three previously published algorithms: the betweenness-based algorithm of Girvan and Newman [10], which is widely used and has been incorporated into some of the more popular network analysis programs (denoted GN in the table); the fast algorithm of Clauset et al. [26] (CNM), which optimizes modularity using a greedy algorithm; and the extremal optimization algorithm of Duch and Arenas [19] (DA), which is arguably the best previously existing method, by standard
measures, if one discounts methods impractical for large networks, such as exhaustive enumeration of all partitions or simulated annealing.

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Learning Analytics – Análisis del aprendizaje

Learning Analytics – Análisis del aprendizaje | Social Network Analysis | Scoop.it
Ayer tuve la ocasión de escuchar la conferencia de George Siemens que dió en Buenos Aires sobre la evolución del paradigma del conectivismo. En el le.
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La interdisciplinariedad del Análisis del Aprendizaje [Learning ...

La interdisciplinariedad del Análisis del Aprendizaje [Learning ... | Social Network Analysis | Scoop.it
El análisis del aprendizaje es la medición, recopilación, análisis y presentación de datos sobre los alumnos y sus contextos (pueden medirse otros.
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Cómo optimizar la conexión entre redes - Noticias de la Ciencia y la Tecnología

Cómo optimizar la conexión entre redes - Noticias de la Ciencia y la Tecnología | Social Network Analysis | Scoop.it

"Tres investigadores del Centro de Astrobiología (CAB, CSIC-INTA), el Centro de Tecnología Biomédica (Universidad Politécnica de Madrid) y la Universidad Rey Juan Carlos, todos en España, han desarrollado una nueva teoría para entender la competición que aparece entre diferentes redes al entrar en conexión, así como para diseñar estrategias óptimas para que cada red se beneficie de las uniones con otras redes."

 

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Science of Winning Soccer: Emergent pattern-forming dynamics in association football

Science of Winning Soccer: Emergent pattern-forming dynamics in association football | Social Network Analysis | Scoop.it

While football (soccer) is alternately known as a sport, a national pastime, or a national obsession, the New England Complex Systems Institute (NECSI) is the first to place soccer in the category of a complex social system. A new study from NECSI uses quantitative analysis to reveal the key team dynamics within a Premier League match.

 

L. Vilar, D. Araújo, K. Davids, Y. Bar-Yam, Science of Winning Soccer: Emergent pattern-forming dynamics in association football. Journal of Systems Science and Complexity (in press).


Via Complexity Digest, ukituki
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Special issue of Journal of ETS on learning analytics (ed. George Siemens)

Enrique Rubio:

 

Completa e interesante referencia sobre 'learning analytics', de la mano de George Siemens.

 

En el actual tránsito hacia la educación digital, resulta imprescendible la incorporación de nuevas 'analíticas' asociadas a las 'trazas digitales' que dejamos (tanto profesores como estudiantes), mediante la extracción de las correspondientes estadísticas y matrices de datos  (propias del tradicional  'Análisis de Redes Sociales' o de indicadores identificados a título propio o estándares), para su tratamiento y visualización gráfica posterior.

 

En el CICEI (http://www.cicei.com/), estamos desarrollando plugins para la extracción de datos (a partir de las mencionadas trazas digitales), de Elgg y Moodle, en formato de matriz utilizado por Pajek, para su proceso y visualización gráfica posterior. Inicialmente, pueden generarse las matrices de amistad y colaboración, pudiéndose analizarse, en el caso de Elgg, recursos tales como: blogs, galería de imágenes, marcadores, foros, videos, páginas, subpáginas, y ficheros. Puede filtrase por fechas y/o tipo de colaboración.

 

Las matrices correspondientes, pueden generarse para los siguientes conjuntos de usuarios: 'Amigos' de un usuario; 'Miembros de un grupo'; o 'Todos los usuarios de la red social', en nuestro caso 'Sociedad y Tecnología' (http://www.sociedadytecnologia.org/).

 

En el caso de Moodle, de forma similar a como lo hace SNAPP (http://research.uow.edu.au/learningnetworks/seeing/snapp/index.html/), generamos matrices de colaboración (en el formato Pajeck), con las interacciones que se producen en los foros (para un foro, para uno o mas foros del curso, o para todos los foros de uno o mas cursos).

 

 

"The Society for Learning Analytics Research defines learning analytics as the measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimizing learning and the environments in which it occurs (http://www.solaresearch.org/mission/about/)."


Via enrique rubio royo, L. García Aretio
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Educational Data Mining 2013 Conference

Educational Data Mining 2013 Conference | Social Network Analysis | Scoop.it

EDM 2013 invites papers that study how to apply data mining to analyze data generated by various information systems supporting learning or education (in schools, colleges, universities, and other academic or professional learning institutions providing traditional and modern forms and means of teaching, as well as informal learning). EDM may require adaptation of existing or development of new approaches that build upon techniques from a combination of areas, including but not limited to statistics, psychometrics, machine learning, information retrieval, recommender systems and scientific computing

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EDM 2013 invites papers that study how to apply data mining to analyze data generated by various information systems supporting learning or education (in schools, colleges, universities, and other academic or professional learning institutions providing traditional and modern forms and means of teaching, as well as informal learning). EDM may require adaptation of existing or development of new approaches that build upon techniques from a combination of areas, including but not limited to statistics, psychometrics, machine learning, information retrieval, recommender systems and scientific computing

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The Social Network of the Planetary Data System: A Comparative Analysis of Network Representations

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