High-Dimensional Expanders - Lecture 2
Explore high-dimensional expanders with Irit Dinur, delving into advanced concepts and applications in analysis and theoretical computer science.
Theory Seminar - Algorithms and Hardness for Linear Algebra on Geometric Graphs, Aaron Schild
Explore algorithms and hardness for linear algebra on geometric graphs, focusing on efficient spectral graph theory and its limitations in high-dimensional spaces.
Explicit Near-Fully X-Ramanujan Graphs
Explore finite graphs resembling infinite ones, focusing on Ramanujan graphs and spectral properties. Learn about matrix-weighted polynomials and permutation-based approaches.
Miracles of Algebraic Graph Theory
Explore algebraic graph theory's fascinating applications in spectral analysis, graph drawing, and network approximation. Discover how mathematical concepts revolutionize our understanding of complex structures.
Node Classification with Graph Convolutional Networks for Graph Machine Learning
Master Graph Convolutional Networks (GCN) implementation in PyG for node classification, featuring practical coding examples and theoretical foundations of spectral graph convolutions.
Introduction to Statistical Network Analysis - Lecture 3
Explore statistical network analysis techniques with Keith Levin. Gain insights into advanced methods for analyzing complex network structures and their applications in various fields.
Introduction to Statistical Network Analysis - Lecture 2
Explore statistical network analysis techniques with Keith Levin. Gain insights into advanced methods for analyzing complex network structures and their applications in various fields.