Graph Mining: Modularity and Community Detection

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Related lectures (29)

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Graph Algorithms: Memory Management and Traversal

Explores memory management, graph representation, and traversal algorithms in Python, emphasizing BFS and DFS.

Handling Network Data

Covers handling network data, types of graphs, centrality measures, and properties of real-world networks.

Union-Find and Prim's Algorithm

Introduces Union-Find data structure and Prim's algorithm for minimum spanning trees in graphs, exploring cuts and historical origins.

Mining Social Graphs

Explores mining social graphs, regularization, community structures, and community detection algorithms in various applications.

Graph Neural Networks: Interconnected World

Explores learning from interconnected data with graphs, covering modern ML research goals, pioneering methods, interdisciplinary applications, and democratization of graph ML.

Algorithms: Union Find and Minimum Spanning Trees

Discusses Union-Find data structures and Minimum Spanning Trees, covering algorithms and their applications in network design and optimization.

Graph Mining: Social Networks Analysis

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

Flow Networks: Understanding Flows and Cuts in Algorithms

Covers flow networks, focusing on flows, cuts, and their applications in algorithms.

Social and Information Networks: Structure

Explores the structure of social and information networks, focusing on giant components, clustering, tie formation, and network connectivity.