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
Self-Supervised Learning on Graphs
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- Self-supervised Learning on Graphs: A Survey, Yanqiao Zhu, Yichen Xu, Feng Yu, Qiang Liu, Shu Wu, and Yuan Qi, 2021 IEEE Transactions on Knowledge and Data Engineering, Vol. 35 (IEEE) DOI: 10.1109/TKDE.2021.3134621 - This survey comprehensively reviews self-supervised learning methods for graphs, categorizing and detailing various techniques.
- Deep Graph Contrastive Representation Learning, Yanqiao Zhu, Yichen Xu, Feng Yu, Qiang Liu, Shu Wu, Liang Wang, 2020 Advances in Neural Information Processing Systems (NeurIPS), Vol. 33 DOI: 10.48550/arXiv.2006.04131 - Introduces GRACE, a method for node-level contrastive learning on graphs, demonstrating the effectiveness of graph augmentations.
- Graph Contrastive Learning with Augmentations, Yuning You, Tianlong Chen, Yongduo Sui, Ting Chen, Zhangyang Wang, Yang Shen, 2020 Advances in Neural Information Processing Systems (NeurIPS), Vol. 33 DOI: 10.48550/arXiv.2010.13902 - Proposes GraphCL, a method for graph-level contrastive learning, exploring various graph augmentation strategies.