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
Knowledge Graph Embeddings with GNNs
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- Modeling Relational Data with Graph Convolutional Networks, Michael Schlichtkrull, Thomas N. Kipf, Peter Bloem, Rianne van den Berg, Ivan Titov, and Max Welling, 2017 Extended Semantic Web Conference (ESWC) DOI: 10.1007/978-3-319-93417-4_29 - Introduces Relational Graph Convolutional Networks (R-GCNs) for modeling knowledge graphs, detailing relation-specific transformations and parameter regularization techniques.
- Composition-based Multi-Relational Graph Convolutional Networks, Shikhar Vashishth, Soumya Sanyal, Vikram Nitin, Partha Talukdar, 2020 International Conference on Learning Representations (ICLR) 2020 DOI: 10.48550/arXiv.1911.03082 - Presents Composition-based Multi-Relational Graph Convolutional Networks (CompGCN), using compositional operators to learn entity and relation embeddings.
- Graph Neural Networks for Knowledge Graph Completion: An Overview, Chengjin Zhang, Fan Zhou, Pengyuan Zhang, Yuqing Xia, Cheng Shi, and Jianlong Tan, 2020 Journal of Artificial Intelligence Research, Vol. 67 (AI Access Foundation) DOI: 10.1613/jair.1.12177 - Comprehensive overview of Graph Neural Networks applied to knowledge graph completion, discussing various architectures and tasks.
- Heterogeneous Graph Attention Network, Xiao Wang, Houye Ji, Chuan Shi, Bai Wang, Yanfang Ye, Peng Cui, Philip S. Yu, 2019 The Web Conference (WWW) (ACM) DOI: 10.1145/3308558.3313562 - Proposes the Heterogeneous Graph Attention Network (HAN), which incorporates attention mechanisms for hierarchical aggregation on heterogeneous graphs.