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Lecture
Graph Mining: Modularity and Community Detection
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Related lectures (42)
Graph Algorithms: Ford-Fulkerson and Strongly Connected Components
Discusses the Ford-Fulkerson method and strongly connected components in graph algorithms.
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Graph Algorithms: Modeling and Traversal
Covers graph algorithms, modeling relationships between objects, and traversal techniques like BFS and DFS.
Fixed Points in Graph Theory
Focuses on fixed points in graph theory and their implications in algorithms and analysis.
Graph Algorithms II: Traversal and Paths
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Graph Algorithms: DFS, Topological Sort, SCC
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Epidemic Spreading Models
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Handling Networks: Graph Theory
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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.
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.
Flow Networks: Understanding Flows and Cuts in Algorithms
Covers flow networks, focusing on flows, cuts, and their applications in algorithms.
Graph Coloring: Theory and Applications
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Explores graph coloring theory, spectral clustering, community detection, and network structures.
Social Network Analysis: Modularity Measure
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Explores the computation of the modularity measure and betweenness centrality in graphs for community detection.
Graph Algorithms: Basics
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Introduces the basics of graph algorithms, covering traversal, representation, and data structures for BFS and DFS.
Graph Algorithms: BFS and DFS
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Explores graph algorithms like BFS and DFS, discussing shortest paths, spanning trees, and data structures' role.
Statistical Analysis of Network Data: Structures and Models
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Explores statistical analysis of network data, covering graph structures, models, statistics, and sampling methods.
Algorithmic Paradigms for Dynamic Graph Problems
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Covers algorithmic paradigms for dynamic graph problems, including dynamic connectivity, expander decomposition, and local clustering, breaking barriers in k-vertex connectivity problems.
Graph Algorithms: Memory Management and Traversal
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Explores memory management, graph representation, and traversal algorithms in Python, emphasizing BFS and DFS.
Mining Social Graphs
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Explores mining social graphs, regularization, community structures, and community detection algorithms in various applications.
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