Hypergraph

Related lectures (25)

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

Introduces hypergraphs, generalizing graphs by allowing subsets of nodes to form edges and exploring their applications in various fields.

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

Explores tactical configurations, covering the minimal size of subsets needed to cover element sets and the concept of K-sets and base points.

Explores set coverings and uniformity theorems in hypergraphs, revealing insights into their structures.

Explores AI applications in chemistry, hypergraphs, single-step ritual synthesis, and retro-synthetic models evaluation.

Explores statistical analysis of network data, covering graph structures, models, statistics, and sampling methods.

Explores topological sort, acyclic graphs, Strongly Connected Components, magic algorithm, component graph, flow networks, and their applications.

Covers girth, independence, probability, union bound, sets, and hypergraph recoloring.

Covers the independence polynomial of a dependency graph and related concepts such as graph coloring and directed graph properties.