Lectures related to Automorphism groups of trees and graphs

Explores the diagonalization of matrices and the conditions for exact diagonalization, with examples demonstrating the process.

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

Explores the convergence of random walks on graphs and the properties of weighted adjacency matrices.

Introduces the basics of graph algorithms, covering traversal, representation, and data structures for BFS and DFS.

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

Covers the Stochastic Block Model for community detection, focusing on the detection of communities, clusters, and groups.

Covers embedding graphs into trees with a focus on minimizing distortion and Bartal Tree embeddings.

Explores fundamental graph theory concepts, Erdős' results, Chromatic Lemma, and Union Bound theorem in graph theory.