Bipartite and n-partite Graphs

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

Covers graph properties, representations, and traversal algorithms using BFS and DFS.

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

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

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

Explores automorphism groups of trees and graphs, including actions on trees and group homomorphisms.

Explores automorphisms of graphs, focusing on automorphism groups, Cayley-Abels graphs, and quasi-isometry.

Explores automorphism groups in trees and graphs, focusing on ends and types of automorphisms.

Explores pseudorandomness theory, AI challenges, pseudo-random graphs, random walks, and matrix properties.

Explores bipartite graphs, independent sets, Shearer's Lemma, labeled graphs, and entropy analysis.