Lectures related to Graph Algorithms: Memory Management and Traversal

Graph Neural Networks: Interconnected World

Explores learning from interconnected data with graphs, covering modern ML research goals, pioneering methods, interdisciplinary applications, and democratization of graph ML.

Laplacian Matrix: Properties and Examples

Explores the Laplacian matrix, time-varying consensus theorems, and balanced graphs in networked control systems.

Consensus in Networked Control Systems

Explores consensus in networked control systems through graph weight design and matrix properties.

Spectral Graph Theory: Introduction

Introduces Spectral Graph Theory, exploring eigenvalues and eigenvectors' role in graph properties.

Introduction to Algorithms

Introduces algorithms as problem-solving procedures, covering complexity, correctness, and implementation in various languages.

Learning from the Interconnected World with Graphs

Explores learning from interconnected data using graphs, covering challenges, GNN design, research landscapes, and democratization of Graph ML.

Graphs and Networks: Basics and Applications

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