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
Deep Graph Library (DGL) Advanced Features
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- Deep Graph Library (DGL) Documentation, DGL Developers, 2024 (DGL Developers) - The official documentation serves as a comprehensive resource for DGL's APIs, tutorials, and detailed explanations of its advanced features, including heterogeneous graph handling, UDFs, and scalable sampling techniques.
- DGL: A Python Package for Graph Neural Networks, Minjie Wang, Da Zheng, Zihao Ye, Quan Gan, Mufei Li, Xiang Song, Jinjing Zhou, Chenglong Li, Jonathan Li, Jiali Yu, Yutong Lu, Rui Zhang, Chuntao Hong, Tianjun Xiao, Haibin Lin, Junbo Li, Zeng Li, Rongchen Li, Wenzhao Lian, Ziyan Zheng, Fei Ye, Chong Chen, John Muradeli, Yi Han, Xiaodong Lin, Lu Guo, Edward Chung, Blaz Skrlj, Jiajie Li, Chengqiang Lu, Pan Li, Jinyang Li, Muhan Zhang, George Karypis, 2019 Advances in Neural Information Processing Systems (NeurIPS) (NeurIPS) DOI: 10.48550/arXiv.1909.01315 - This foundational paper introduces the Deep Graph Library (DGL) framework, detailing its architecture and design principles that enable efficient and flexible implementation of GNNs, including support for various graph types and operations discussed in the section.
- A Survey on Heterogeneous Graph Neural Networks, Shirui Pan, Yuanning Li, Guodong Long, Jing Jiang, Lin Zhu, Chengqiang Lu, 2021 IEEE Transactions on Knowledge and Data Engineering, Vol. 34 (IEEE) DOI: 10.1109/TKDE.2021.3090432 - This survey provides a comprehensive analysis of heterogeneous graph neural networks (HGNNs), offering context and theoretical grounding for DGL's first-class support for heterogeneous graphs and its specialized modules like
HeteroGraphConv. - Inductive Representation Learning on Large Graphs, William L. Hamilton, Rex Ying, Jure Leskovec, 2017 Advances in Neural Information Processing Systems (NeurIPS), Vol. 30 (Neural Information Processing Systems) DOI: 10.48550/arXiv.1706.02216 - Introduces GraphSAGE, a framework for inductive representation learning using neighbor sampling, a fundamental technique for scaling GNNs to large graphs, directly relevant to DGL's
NeighborSampler. - GraphSAINT: Graph Sampling Based Inductive Learning Method, Hanqing Zeng, Hongkuan Zhou, Ajitesh Srivastava, Rajgopal Kannan, Viktor Prasanna, 2020 International Conference on Learning Representations (ICLR) DOI: 10.48550/arXiv.1907.04931 - Presents GraphSAINT, a full-graph sampling method for training GNNs on large graphs, offering an alternative to neighbor sampling, which DGL implements with
SAINTSampler.