Lectures related to Union and Join Operations

Graphical Models: Representing Probabilistic Distributions

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

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

Covers regular and self-complementary graphs, exploring their properties and examples.

Graphs and Networks: Basics and Applications

Introduces the basics of graphs and networks, covering definitions, paths, trees, flows, circulation, and spanning trees.

Automorphism Groups: Trees and Graphs III

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

Automorphism groups of trees and graphs

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

Graphs: Properties and Representations

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

Pseudorandomness: Theory and Applications

Explores pseudorandomness theory, AI challenges, pseudo-random graphs, random walks, and matrix properties.

Bipartite and n-partite Graphs

Covers bipartite and n-partite graphs, including complete graph definitions and examples.

Graphs in Deep Learning: Applications and Techniques

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

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.

Renormalization: AQFT

Covers the concept of renormalization in Algebraic Quantum Field Theory.

Statistical Analysis of Network Data: Hypergraphs

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

Graph Algorithms: Modeling and Traversal

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

Real Functions: Graphs and Properties

Explores real functions, their graphs, properties, and transformations, including symmetry and surjection.

Sparsest Cut: Bourgain's Theorem

Explores Bourgain's theorem on sparsest cut in graphs, emphasizing semimetrics and cut optimization.

Iterated Integrals: Order and Domains

Explores iterated integrals, order, domains, and symmetrical roles of variables in 2D space.

Handling Network Data

Explores handling network data, including types of graphs, real-world network properties, and node importance measurement.

Handling Network Data

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

Topological Scattering: From Graphs to Networks

Covers defining topological phases for photonic systems using unitary scattering matrices, transitioning from graphs to networks.