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
Subgraph and Clustering Methods (Cluster-GCN)
Was this section helpful?
- Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks, Wei-Lin Chiang, Meng Qu, Xisen Jin, Stein Lim, Jure Leskovec, S. P. K. Shen, 2019 Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD) (ACM) DOI: 10.1145/3292500.3330989 - The original paper introducing the Cluster-GCN method, outlining its graph partitioning strategy and stochastic training approach for large graphs.
- Semi-supervised Classification with Graph Convolutional Networks, Thomas N. Kipf, Max Welling, 2017 International Conference on Learning Representations (ICLR) DOI: 10.48550/arXiv.1609.02907 - Introduces the Graph Convolutional Network (GCN) architecture, providing a basis for the GNN mathematical update formulations discussed.
- A Fast and High Quality Multilevel Scheme for Partitioning Irregular Graphs, George Karypis, Vipin Kumar, 1998 SIAM Journal on Scientific Computing, Vol. 20 (Society for Industrial and Applied Mathematics) DOI: 10.1137/S1064827595287997 - Details the METIS algorithm, a frequently used graph partitioning method applied in Cluster-GCN's initial preprocessing.