Social Network Analysis: Modularity Measure

Related lectures (30)

Page 3 of 3

Explores graph theory in brain connectomics, MRI applications, network analysis relevance, and individual fingerprinting.

Explores the properties and applications of Cayley graphs in group theory and network analysis.

Explores graph coloring theory, spectral clustering, community detection, and network structures.

Covers graph properties, representations, and traversal algorithms using BFS and DFS.

Introduces network data structures, models, and analysis techniques, emphasizing permutation invariance and Erdős-Rényi networks.

Explores graph mining in social networks, covering modularity algorithms and community detection.

Covers graphical models for probabilistic distributions using graphs, nodes, and edges.

Introduces the basics of graph algorithms, covering traversal, representation, and data structures for BFS and DFS.

Explores causal inference through learning graph structures for causal reasoning from observational data.

Introduces Spectral Graph Theory, exploring eigenvalues and eigenvectors' role in graph properties.