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
Graph Pooling and Readout Functions
Was this section helpful?
- Hierarchical Graph Representation Learning with Differentiable Pooling, Zhitao Ying, Jiaxuan You, Christopher Morris, Xiang Ren, William L. Hamilton, Jure Leskovec, 2018 Advances in Neural Information Processing Systems (NeurIPS), Vol. 31 (NeurIPS) DOI: 10.48550/arXiv.1806.08804 - Introduces Differentiable Pooling (DiffPool) for hierarchical graph coarsening, a learned pooling method.
- Self-Attention Graph Pooling, Junhyun Lee, Inyeop Lee, Jaewoo Kang, 2019 Proceedings of Machine Learning Research (PMLR) - International Conference on Machine Learning (ICML), Vol. 97 (PMLR) DOI: 10.48550/arXiv.1904.08082 - Presents Self-Attention Graph Pooling (SAGPool), which uses GNNs to learn attention scores for node selection.
- An End-to-End Deep Learning Architecture for Graph Classification, Muhan Zhang, Zhicheng Cui, Marion Neumann, Yixin Chen, 2018 Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 32(1) (AAAI Press) DOI: 10.1609/aaai.v32i1.11782 - Proposes SortPool, a pooling method that sorts node embeddings based on a canonical order before applying convolutional layers.
- Order Matters: Sequence to Sequence for Sets, Oriol Vinyals, Samy Bengio, Manjunath Kudlur, 2017 International Conference on Learning Representations (ICLR) - Introduces the Set2Set mechanism, a foundational technique used for attention-based graph readout functions to produce permutation-invariant representations.