Advanced Graph Neural Networks: Architectures and Implementation
Chapter 1: Revisiting Graph Neural Network Foundations
Graph Representations for Machine Learning
The Message Passing Framework Revisited
Spectral Graph Theory Fundamentals for GNNs
Graph Signal Processing Concepts
Expressive Power and the WL Test
Chapter 2: Advanced Graph Neural Network Architectures
Graph Convolutional Networks (GCN)
Graph Attention Networks (GAT)
Implementing Multi-Head Attention in GATs
Graph Transformers
Advanced Spectral GNNs (ChebNet, CayleyNets)
Advanced Spatial GNNs (GraphSAGE Variants, PNA)
Comparing Architectural Choices and Trade-offs
Hands-on Practical: Implementing GAT Layers
Chapter 3: Addressing Training Complexities in GNNs
The Oversmoothing Problem
Techniques to Mitigate Oversmoothing
The Oversquashing Problem
Handling Large Graphs: Scalability Challenges
Neighborhood Sampling Techniques (GraphSAGE)
Graph Sampling Techniques (GraphSAINT, ShaDow-GNN)
Subgraph and Clustering Methods (Cluster-GCN)
Optimization Strategies for GNNs
Practice: Applying Scalable GNN Training
Chapter 4: Advanced GNN Tasks and Techniques
Handling Heterogeneous Graphs (RGCN, HAN)
Modeling Dynamic and Temporal Graphs
Knowledge Graph Embeddings with GNNs
Graph Generative Models
Self-Supervised Learning on Graphs
Graph Pooling and Readout Functions
Hands-on Practical: Heterogeneous Graph Node Classification
Chapter 5: GNN Implementation, Tooling, and Optimization
Deep Graph Library (DGL) Advanced Features
PyTorch Geometric (PyG) Advanced Features
Efficient Sparse Matrix Operations for GNNs
GPU Acceleration and Memory Management
Debugging and Visualizing GNNs
Benchmarking and Performance Tuning
Integrating GNNs into Production Systems
Hands-on Practical: Optimizing a GNN Implementation
Efficient Sparse Matrix Operations for GNNs
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- Semi-Supervised Classification with Graph Convolutional Networks, Thomas N. Kipf and Max Welling, 2017 International Conference on Learning Representations (ICLR) DOI: 10.48550/arXiv.1609.02907 - This paper introduces Graph Convolutional Networks (GCNs), a widely referenced GNN architecture where the message passing scheme computationally benefits from efficient sparse matrix operations.
- Templates for the Solution of Sparse Linear Systems, Richard Barrett, Michael Berry, Tony F. Chan, James Demmel, June Donato, Jack Dongarra, Victor Eijkhout, Roldan Pozo, Charles Romine, Henk van der Vorst, 1994 (SIAM) DOI: 10.1137/1.9781611970898 - A comprehensive resource detailing various sparse matrix formats (COO, CSR, CSC) and algorithms for sparse linear algebra, which underlie many GNN operations. While older, its content on sparse matrix principles remains relevant.
- PyTorch Geometric, Matthias Fey, Jan Eric Lenssen, 2019 ICLR 2019 (RLGM Workshop) DOI: 10.48550/arXiv.1903.02428 - This paper introduces PyTorch Geometric, a popular library for GNNs, which is designed with efficient sparse tensor operations, particularly Sparse Matrix-Dense Matrix Multiplication (SpMM), for processing graph data.
- Deep Graph Library: A Graph-Centric, User-Friendly, and High-Performance Package for GNNs, Minjie Wang, Da Zheng, Zihao Ye, Quan Gan, Mufei Li, Xiang Song, Jinjing Zhou, Chao Ma, Lingfan Yu, Yu Gai, Tianjun Xiao, Tong He, George Karypis, Jinyang Li, Zheng Zhang, 2019 International Conference on Learning Representations (ICLR) Workshop on Deep Learning on Graphs: Methodologies and Applications DOI: 10.48550/arXiv.1909.01315 - This work presents the Deep Graph Library (DGL), a versatile and high-performance framework for GNNs, which manages and optimizes sparse graph representations internally for various operations.