Graph Mining: Modularity and Community Detection

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Explores the computation of the modularity measure and betweenness centrality in graphs for community detection.

Covers classical models of epidemic spreading and dynamics on networks with examples.

Covers the basics of graph algorithms, focusing on modeling and representation of graphs in memory.

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

Explores graph algorithms like BFS and DFS, discussing shortest paths, spanning trees, and data structures' role.

Explores statistical analysis of network data, covering graph structures, models, statistics, and sampling methods.

Explores graph theory concepts, centrality measures, and real-world network properties, providing insights into handling diverse types of networks.

Covers stochastic properties, network structures, models, statistics, centrality measures, and sampling methods in network data analysis.

Explores link-based ranking, document classification, and graph mining techniques.

Introduces graph theory, network flows, and flow conservation laws with practical examples and theorems.