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Related lectures (32)

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Statistical Analysis of Network Data

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

Graphical models: Inference and Factor Graphs

Explores graphical models, factor graphs, and probabilistic inferences in complex systems.

Graphical Models: Representing Probabilistic Distributions

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

Automorphism groups: Trees and Graphs

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

Automorphism groups of trees and graphs II

Explores the uniqueness of trees, automorphism groups, Cayley-Abels graphs, and constructing vertex-transitive subgroups with prescribed local actions.

Belief Propagation

Explores Belief Propagation in graphical models, factor graphs, spin glass examples, Boltzmann distributions, and graph coloring properties.

Matroids: Matroid Intersection

Covers the concept of matroids, focusing on matroid intersection and the properties of subsets of a ground set.

Automorphism Groups: Trees and Graphs III

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

Handling Network Data

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

Graph Algorithms: Modeling and Traversal

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