Social Network Analysis. MAGoLEGO course.

Spring 2017. Module 4

Department of Data Analysis and Artificial Intelligence, School of Computer Science.

Instructors: Prof. Leonid Zhukov, Dr. Ilya Makarov
Course page at cs.hse: Magolego Social Network Analysis

Course Outline

  1. Introduction to network science
  2. Descriptive network analysis
  3. Mathematical models of networks
  4. Node centrality and ranking on networks
  5. Network communities
  6. Network structure and visualization
  7. Epidemics and information spreading in networks
  8. Diffusion of innovation
  9. Strategic network formation
  10. Spatial models of segregation


  1. [07.04.2017] Introduction to network science. [Lecture 1] [Video]
    Introduction to network science. Examples.
  2. [14.04.2017] Descriptive network analysis. [Lecture 2] [Video]
    Basic 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.
  3. [21.04.2017] Mathematical models of networks. [Lecture 3][Video]
    Erdos-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
  4. [28.04.2017] Node centrality and ranking on networks. [Lecture 4] [Video]
    Node centrality metrics, degree centrality, closeness centrality, betweenness centrality, eigenvector centrality. Katz status index and Bonacich centrality, alpha centrality PageRank,Hubs and Authorites.
  5. [12.05.2017] Network communities [Lecture 5] [Video]
    Cohesive subgroups. Graph cliques. Network communities. Graph partitioning. Modularity. Edge Betweenness. Spectral partitioning. Modularity maximization. Heuristic methods. Label propagation. Fast community unfolding. Walktrap.
  6. [19.05.2017] Network structure and visualization [Lecture 6] [Video from 2016 course offerning]
  7. k-core decomposition of networks. Diads and triads. Edge reciprocity. Frequent subgraphs. Network motifs. Assortative mixing. Network visualization. Forde directed layouts. Adjacency matrix ordering.
  8. [26.05.2017] Epidemics and information spreading in networks [Lecture 7] [Video]
    Epidemic models on networks. SI, SIS, SIR models. Rumor spreading. Propagation trees.
  9. [02.06.2017] Diffusion of innovation [Lecture 8] [Video]
    Diffusion of innovation. Linear threshold model. Influence maximization.
  10. [09.06.2017] Spatial models of segregation [Lecture 9][Video]
    Schelling's segregation model. Spatial segregation. Agent based modelling. Segregation in networks
  11. [16.06.2017] Exam

Labs & Homeworks

Available at Magolego Social Network Analysis

Reading material



SNA courses online