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
PyTorch Geometric (PyG) Advanced Features
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- PyTorch Geometric Documentation, Matthias Fey, Jan E. Lenssen, et al., 2024 (PyTorch Geometric Contributors) - Official documentation for PyTorch Geometric, offering detailed explanations, API references, and examples for its features, including advanced data handling, transforms, and loaders.
- Open Graph Benchmark: Datasets for Machine Learning on Graphs, Weihua Hu, Matthias Fey, Marinka Zitnik, Yuxiao Dong, Hongyu Ren, Bowen Liu, Michele Catasta, Jure Leskovec, 2020 Advances in Neural Information Processing Systems (NeurIPS) 33 (NeurIPS) DOI: 10.48550/arXiv.2005.00687 - Presents the Open Graph Benchmark, a collection of challenging, large-scale graph datasets, including those referenced (e.g., ogbn-arxiv, OGB_MAG), for developing and evaluating GNNs.
- Inductive Representation Learning on Large Graphs, William L. Hamilton, Rex Ying, Jure Leskovec, 2017 Advances in Neural Information Processing Systems, Vol. 30 (Neural Information Processing Systems) DOI: 10.48550/arXiv.1706.02216 - Presents GraphSAGE, an inductive framework for generating node embeddings through sampling and aggregating features from a node's local neighborhood, underpinning PyG's NeighborLoader.
- Heterogeneous Graph Transformer, Ziniu Hu, Yuxiao Dong, Kuansan Wang, Yizhou Sun, 2020 The Web Conference (WWW) DOI: 10.48550/arXiv.2003.01332 - Introduces the Heterogeneous Graph Transformer (HGT), a GNN architecture for heterogeneous graphs, directly relevant to PyG's HGTConv layer.