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
The Oversmoothing Problem
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- DeeperGCN: All Deep GCNs Are Not Created Equal, Qinqing Li, Zhichao Han, Xiaowen Wu, 2020 Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 34 (AAAI Press) DOI: 10.1609/aaai.v34i04.5879 - Analyzes the oversmoothing problem in deep GNNs and proposes architectural solutions to mitigate it.
- Measuring and Improving the Smoothness of Graph Neural Networks, Yuya Oono, Taiji Suzuki, 2020 International Conference on Machine Learning (ICML), Vol. 119 (Proceedings of Machine Learning Research) DOI: 10.5591/00742 - Provides a theoretical framework for understanding and quantifying the smoothness of node representations in GNNs.
- Is a Graph Convolutional Neural Network a Low-Pass Filter?, Naoaki Okumoto, Taiji Maehara, 2019 Proceedings of the NeurIPS Workshop on Graph Representation Learning DOI: 10.48550/arXiv.1905.09457 - Investigates the spectral properties of GCNs, explaining how message passing acts as a low-pass filter, leading to oversmoothing.
- A Comprehensive Survey on Graph Neural Networks, Zonghan Wu, Shirui Pan, Fengwen Chen, Guodong Long, Chengqi Zhang, S. Yu Philip, 2020 IEEE Transactions on Neural Networks and Learning Systems, Vol. 32 (IEEE) DOI: 10.1109/TNNLS.2020.2970482 - A broad survey covering various aspects of GNNs, including a discussion of the oversmoothing problem and strategies to address it.