Graph Mining: Modularity and Community Detection

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Related lectures (29)

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Algorithmic Paradigms for Dynamic Graph Problems

Covers algorithmic paradigms for dynamic graph problems, including dynamic connectivity, expander decomposition, and local clustering, breaking barriers in k-vertex connectivity problems.

Graph Algorithms: Ford-Fulkerson and Strongly Connected Components

Discusses the Ford-Fulkerson method and strongly connected components in graph algorithms.

Handling Network Data

Explores handling network data, including types of graphs, real-world network properties, and node importance measurement.

Graph Algorithms: Modeling and Traversal

Covers graph algorithms, modeling relationships between objects, and traversal techniques like BFS and DFS.

Handling Networks: Graph Theory

Covers the fundamentals of handling networks and centrality measures in graph theory.

Fixed Points in Graph Theory

Focuses on fixed points in graph theory and their implications in algorithms and analysis.

Graph Algorithms: Flows and Strongly Connected Components

Discusses graph algorithms, focusing on flow networks and strongly connected components.

Graph Coloring: Theory and Applications

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

Graph Algorithms II: Traversal and Paths

Explores graph traversal methods, spanning trees, and shortest paths using BFS and DFS.

Graph Algorithms: DFS, Topological Sort, SCC

Explores DFS, Topological Sort, SCC in graphs, and introduces Flow Networks with practical examples.