SOCIAL NETWORK ANALYSIS

Catalog description

This course provides students with essential analyzing and modeling techniques for understanding and extracting information from online social networks such as Facebook, LinkedIn, and Twitter. Students will learn how to apply the basics of social network analysis at the node (ego) level (degree, betweenness, closeness, eigenvector centralities, PageRank, neighbors, and bridges); at the group (sub-graph) level (cliques, clustering coefficient, triadic analysis, structural holes, brokerage, transitivity, and hierarchical clustering); and at the network level (degree distribution, components and isolates, cores and periphery, network density, shortest paths, reciprocity, affiliation networks and two-mode networks, and homophily). Prerequisites: COMP2000 (4 credits)