Social Network Analysis

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Source : www.coursera.org

9 séquences

Niveau : Introductif

Langue : Anglais

360 points

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Les infos clés

Formation gratuite

Certification gratuite

45 heures de cours

En résumé

This course will use social network analysis, both its theory and computational tools, to make sense of the social and information networks that have been fueled and rendered accessible by the internet.

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Le programme

Week 1: What are networks and what use is it to study them?

Concepts: nodes, edges, adjacency matrix, one and two-mode networks, node degree

Activity: Upload a social network (e.g. your Facebook social network into Gephi and visualize it ).

Week 2: Random network models: Erdos-Renyi and Barabasi-Albert

Concepts: connected components, giant component, average shortest path, diameter, breadth-first search, preferential attachment

Activities: Create random networks, calculate component distribution, average shortest path, evaluate impact of structure on ability of information to diffuse

Week 3: Network centrality

Concepts: betweenness, closeness, eigenvector centrality (+ PageRank), network centralization

Activities: calculate and interpret node centrality for real-world networks (your Facebook graph, the Enron corporate email network, Twitter networks, etc.)

Week 4: Community

Concepts: clustering, community structure, modularity, overlapping communities

Activities: detect and interpret disjoint and overlapping communities in a variety of networks (scientific collaborations, political blogs, cooking ingredients, etc.)

Week 5: Small world network models, optimization, strategic network formation and search

Concepts: small worlds, geographic networks, decentralized search

Activity: Evaluate whether several real-world networks exhibit small world properties, simulate decentralized search on different topologies, evaluate effect of small-world topology on information diffusion.

Week 6: Contagion, opinion formation, coordination and cooperation

Concepts: simple contagion, threshold models, opinion formation

Activity: Evaluate via simulation the impact of network structure on the above processes

Week 7: Cool and unusual applications of SNA

Hidalgo et al. : Predicting economic development using product space networks (which countries produce which products)

Ahn et al., and Teng et al.: Learning about cooking from ingredient and flavor networks

Lusseau et al.: Social networks of dolphins

Activity: hands-on exploration of these networks using concepts learned earlier in the course

Week 8: SNA and online social networks

Concepts: how services such as Facebook, LinkedIn, Twitter, CouchSurfing, etc. are using SNA to understand their users and improve their functionality

Activity: read recent research by and based on these services and learn how SNA concepts were applied

Les intervenants

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Lada Adamic - School of Information, Center for the Study of Complex Systems

Le concepteur

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L'Université du Michigan (UM, UMich ou simplement Michigan) est une université publique de recherche située à Ann Arbor, dans le Michigan, aux États-Unis. Fondée en 1817, l'université est la plus ancienne et la plus grande du Michigan.

La mission de l'université du Michigan est de servir les habitants du Michigan et le monde entier en occupant une place prépondérante dans la création, la communication, la préservation et l'application des connaissances, de l'art et des valeurs académiques, et en formant des dirigeants et des citoyens qui défieront le présent et enrichiront l'avenir.

La plateforme

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Coursera est une entreprise numérique proposant des formations en ligne ouverte à tous fondée par les professeurs d'informatique Andrew Ng et Daphne Koller de l'université Stanford, située à Mountain View, Californie.

Ce qui la différencie le plus des autres plateformes MOOC, c'est qu'elle travaille qu'avec les meilleures universités et organisations mondiales et diffuse leurs contenus sur le web.

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