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
GPU Acceleration and Memory Management
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- CUDA C++ Programming Guide, NVIDIA Corporation, 2024 (NVIDIA Corporation) - Provides fundamental information on GPU architecture and parallel programming techniques using CUDA C++.
- PyTorch Geometric Documentation, Matthias Fey and Jan E. Lenssen, 2024 - Offers practical instructions and examples for using PyTorch Geometric, including GPU acceleration.
- Deep Graph Library (DGL) Documentation, Deep Graph Library Contributors, 2024 - Contains resources for effectively utilizing the Deep Graph Library, covering GPU acceleration and graph handling.
- Mixed-Precision Training, Paulius Micikevicius, Sharan Narang, Jonah Alben, Gregory Diamos, Erich Elsen, David Garcia, Boris Ginsburg, Michael Houston, Oleksii Kuchaiev, Ganesh Venkatesh, Hao Wu, 2018 ICLR 2018 DOI: 10.48550/arXiv.1710.03740 - Introduces the method of mixed-precision training using half-precision floating points (FP16) to reduce memory consumption and accelerate deep learning models.
- Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks, Wei-Lin Chiang, Xuanqing Liu, Si Si, Yang Li, Samy Bengio, Cho-Jui Hsieh, 2019 KDD '19: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (ACM) DOI: 10.1145/3292500.3330925 - Presents an efficient mini-batch training algorithm for graph convolutional networks that improves scalability by sampling subgraphs through graph clustering.