MyPage is a personalized page based on your interests.The page is customized to help you to find content that matters you the most.

I'm not curious

Social Network Analysis

Course Summary

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.

  • +

    Course Syllabus

    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

  • +

    Recommended Background

    There are no math or programming prerequisites for the class. There will be a few additional assignments for those with a programming background, which will use the R statistical programming language along with NetLogo.

  • +

    Course Format

    The class will consist of lecture videos, which are between 8 and 12 minutes in length. These contain 1-2 integrated quiz questions per video. There will also be standalone homeworks that are not part of video lectures, optional programming assignments, and a (not optional) final exam.

  • +

    Suggested Reading

    If you’d like to get a head start, download Gephi and explore some of its tutorials. To explore networks interactively, you can visit the NetLogo demonstrations. If you’re itching to read, the Easley and Kleinberg free text on Networks, Crowds and Markets is excellent. The chapters pertinent to this class are 1-5, 13-14,19-21.

Course Fee:

Course Type:


Course Status:



1 - 4 hours / week

Attended this course?

Back to Top

Awards & Accolades for MyTechLogy
Winner of
Top 100 Asia
Finalist at SiTF Awards 2014 under the category Best Social & Community Product
Finalist at HR Vendor of the Year 2015 Awards under the category Best Learning Management System
Finalist at HR Vendor of the Year 2015 Awards under the category Best Talent Management Software
Hidden Image Url

Back to Top