Social Network Analysis
- Providing students with essential knowledge of network analysis applicable to real world data, with examples from today’s most popular social networks.
- Students know basic notation and terminology used in network science.
- Students understand basic principles behind network analysis algorithms.
- Students develop practical skills of network analysis in R programming language.
- Students visualize, summarize and compare networks.
- Students analyze real work networks.
- Introduction to network scienceIntroduction to network science. Examples.
- Descriptive network analysisBasic graph theory notations. Node degree. Node degree distribution. Power laws. Scale free networks. Connected components. Graph diameter. Average path length. Local and global clustering coefficients. Transitivity.
- Mathematical models of networksErdos-Reni random graph model. Bernoulli distribution. Phase transition, gigantic connected component. Diameter and cluster coefficient. Barabasi-Albert model. Preferential attachement. Small world model. Watts-Strogats model. Transition from regular to random.
- Node centralitiy and ranking on networkNode centrality metrics, degree centrality, closeness centrality, betweenness centrality, eigenvector centrality. Katz status index and Bonacich centrality, alpha centrality PageRank,Hubs and Authorites.
- Network communitiesCohesive subgroups. Graph cliques. Network communities. Graph partitioning. Modularity. Edge Betweenness. Spectral partitioning. Modularity maximization. Heuristic methods. Label propagation. Fast community unfolding. Walktrap.
- Epidemics and information spreading in networksEpidemic models on networks. SI, SIS, SIR models. Rumor spreading. Propagation trees.
- Diffusion of innovationDiffusion of innovation. Linear threshold model. Influence maximization.
- Spatial models of segregationSchelling's segregation model. Spatial segregation. Agent based modelling. Segregation in networks.
- Interim assessment (4 module)0.4 * Exam + 0.15 * Homework 1 + 0.15 * Homework 2 + 0.15 * Homework 3 + 0.15 * Homework 4
- Easley, D. et al. Networks, crowds, and markets. – Cambridge : Cambridge university press, 2010. – 744 pp.
- Kolaczyk E. D., Csárdi G. Statistical analysis of network data with R. – New York : Springer, 2014. – 207 pp.
- Barabási A. L., Frangos J. Linked: the new science of networks science of networks. – Basic Books, 2002. – 211 pp.
- Zuur, A., Ieno, E. N., Meesters E. A Beginner's Guide to R. – Springer Science & Business Media, 2009. – 240 pp.