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
Advanced Spatial GNNs (GraphSAGE Variants, PNA)
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- Inductive Representation Learning on Large Graphs, William L. Hamilton, Rex Ying, Jure Leskovec, 2017 Advances in Neural Information Processing Systems DOI: 10.48550/arXiv.1706.02216 - Presents the original GraphSAGE framework for inductive node embedding generation, introducing aggregation functions like mean, max-pooling, and LSTM for learning on large graphs.
- Principal Neighbourhood Aggregation for Graph Neural Networks, Gabriele Corso, Luca Cavalleri, Dominique Beaini, Pietro Liò, Petar Veličković, 2020 Advances in Neural Information Processing Systems DOI: 10.48550/arXiv.2004.05718 - Proposes Principal Neighbourhood Aggregation, a GNN layer that combines multiple aggregation functions and degree-based scalers to capture richer neighborhood information and handle diverse node degrees effectively.
- Graph Attention Networks, Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, Yoshua Bengio, 2017 International Conference on Learning Representations DOI: 10.48550/arxiv.1710.10903 - Introduces the Graph Attention Network, a neural network architecture that leverages masked self-attentional layers to learn weighted neighbor contributions, influencing subsequent attention-based GNN variants.