Dive into an advanced lecture on Graph Convolutional Networks (GCNs) that explores fundamental concepts of graph convolution operations and their practical implementations. Learn how convolution principles are applied to graph structures, understand the mathematical foundations of Vanilla Spectral GCNs, and master the intricacies of ChebNet architecture. Explore the innovative use of Chebyshev polynomials in graph convolution operations while gaining insights into various ConvGNN models. Discover how these theoretical frameworks translate into practical applications for processing graph-structured data, with detailed explanations of both basic and advanced concepts in graph neural networks.
Graph Neural Networks and Convolutional Networks - Part 1 - Lecture 18
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Overview
Syllabus
Ali Ghodsi, Deep Learning, Graph Neural Newark (Part 1), Fall 2023, Lecture 18
Taught by
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