Statistical Analysis of Network Data: Hypergraphs

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Explores directed networks with asymmetric relationships and hypergraphs that generalize graphs by allowing edges to connect any subset of nodes.

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

Explores the Stein algorithm for polynomial identity testing and the minimization of a cut problem.

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

Covers hypergraphs, complete hypergraphs, link prediction, and scoring methods in network data analysis.

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

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

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

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

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