Why?
Social networks, the web and many business systems are graphs. Network analysis gives you the tools to measure influence, find communities and predict new connections.
What?
Social network analysis: graph foundations and history, centrality measures, modelling and visualization, frequent pattern mining for network construction, HITS and PageRank, community discovery with cliques, search engine optimization, link prediction, Web 2.0 and social media mining.
Curriculum:
Introduction to Social Networks
Actors, links and sociograms; why graph theory underpins SNA; a brief history of the field.
Centrality Measures
Degree, closeness, betweenness and other measures of node importance, with examples.
Modeling and Visualization
User-centric visualization, email groups and building networks from data, including from frequent pattern mining.
Hubs, Authorities and PageRank
The HITS algorithm and Google's PageRank for ranking web pages.
Community Discovery
Cliques, clique percolation and other approaches for finding communities.
Search Engine Optimization
How search engines work and the content and link practices that improve ranking.
Link Prediction
Predicting future or missing links with path based and neighbourhood based features, and the challenges involved.
Web 2.0 and Social Media Mining
The social web, major platforms, and mining social media data for applications.
Notes
Slide-based lectures with many worked graph examples. The course has a 2026 edition; past exam questions and a student summary are in the shared files.