SYLLABUS:-
UNIT-I
Social network analysis: network definition, manipulation, calculation, visualization. Graph terminology and definitions.
Representing networks: Adjacency matrix and properties. Weighted, directed, bipartite networks. Trees. Some sample networks.
UNIT-II
Linear Algebra / Graph Properties: Eigenvectors and eigenvalues. Graph Laplacian. Markov matrices. Paths, walks, cycles. Degree, density. Degree distribution. Diameter, average path length. Average and local clustering. Centrality measures:degree, betweenness, closeness, Katz, Bonacich. Review of Poisson random graphs. Growing random networks. Preferential attachment. Properties and phase transitions. Degree distributions. Fitting networks to data. Exponential random graph models.
UNIT-III
Frameworks for evaluating results in network analysis: autocorrelation, matching techniques, QAP regression, exponential random graphs, and other models. Computational considerations. Lab: Applying ERGM analysis. Graph partitioning. Spectral partitioning. Modularity and modularity maximization. Betweenness clustering. Lab: Calculating and comparing clustering approaches.
UNIT-IV
Game theory basics: players, moves, payoffs. Nash equilibrium. Efficiency and optimality. Examples. Network formation as a game. Pairwise stability. Positive and negative externalities. Processes on Networks: Diffusion on networks. SIS and SIR infection models and predictions. Search on networks. Networked adoption games.
UNIT-I
Social network analysis: network definition, manipulation, calculation, visualization. Graph terminology and definitions.
Representing networks: Adjacency matrix and properties. Weighted, directed, bipartite networks. Trees. Some sample networks.
UNIT-II
Linear Algebra / Graph Properties: Eigenvectors and eigenvalues. Graph Laplacian. Markov matrices. Paths, walks, cycles. Degree, density. Degree distribution. Diameter, average path length. Average and local clustering. Centrality measures:degree, betweenness, closeness, Katz, Bonacich. Review of Poisson random graphs. Growing random networks. Preferential attachment. Properties and phase transitions. Degree distributions. Fitting networks to data. Exponential random graph models.
UNIT-III
Frameworks for evaluating results in network analysis: autocorrelation, matching techniques, QAP regression, exponential random graphs, and other models. Computational considerations. Lab: Applying ERGM analysis. Graph partitioning. Spectral partitioning. Modularity and modularity maximization. Betweenness clustering. Lab: Calculating and comparing clustering approaches.
UNIT-IV
Game theory basics: players, moves, payoffs. Nash equilibrium. Efficiency and optimality. Examples. Network formation as a game. Pairwise stability. Positive and negative externalities. Processes on Networks: Diffusion on networks. SIS and SIR infection models and predictions. Search on networks. Networked adoption games.