Lectures related to Algebraic Graph Theory: Matrices and Connectivity

Explores pseudorandomness and the Expander Mixing Lemma in the context of d-regular graphs.

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

Covers topics like DFS output, edge classification, acyclic graphs, correctness, time analysis, SCCs, and the Topological Sort algorithm.

Explores coordination among agents in networked control systems through graph theory and real-world examples.

Explores the properties of undirected and directed graphs, emphasizing connectivity and network topology modeling.

Explains isomorphic graphs through examples and the relationship with identical graphs.

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

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