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
Debugging and Visualizing GNNs
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- The Devil Is in the Details: A Framework for Understanding and Improving Graph Neural Networks, Chuang Zhang, Donglin Song, Chenyang Huang, Kaili Shi, Cheng Li, 2021 International Conference on Learning Representations (ICLR) DOI: 10.48550/arXiv.2007.03964 - Discusses common pitfalls and best practices in implementing and training GNNs, providing valuable insights for debugging training processes and addressing issues like over-smoothing.
- GNNExplainer: Generating Explanations for Graph Neural Networks, Zhitao Ying, Dylan Bourgeois, Jiaxuan You, Marinka Zitnik, Jure Leskovec, 2019 Advances in Neural Information Processing Systems, Vol. 32 - Introduces a method for explaining GNN predictions by identifying important graph structures and features, which is essential for visualizing and interpreting GNN behavior.
- CS224W: Machine Learning with Graphs, Jure Leskovec, 2024 (Stanford University) - Provides lecture notes, assignments, and practical advice on implementing, debugging, and understanding Graph Neural Networks, serving as an educational resource for practical GNN development.