Graphs: Properties and Representations

Related lectures (29)

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

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

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

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

Explores Prim's algorithm for minimum spanning trees and introduces the Traveling Salesman Problem.

Explores the role of graphs in deep learning, focusing on their structure, applications, and techniques for processing graph data.

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

Explores expanders, Ramanujan graphs, eigenvalues, Laplacian matrices, and spectral properties.

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

Introduces elementary graph algorithms, focusing on Breadth-First Search and Depth-First Search.