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 Generative Models
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- GraphRNN: Generating Realistic Graphs with Deep Generative Models, Jiaxuan You, Rex Ying, Xiang Ren, William Hamilton, Jure Leskovec, 2018 Proceedings of the 35th International Conference on Machine Learning, Vol. 80 (PMLR) DOI: 10.5555/3326922.3326932 - This paper introduces GraphRNN, a pioneering autoregressive model for realistic graph generation.
- Variational Graph Auto-Encoders, Thomas N. Kipf, Max Welling, 2016 arXiv preprint arXiv:1611.07308 DOI: 10.48550/arXiv.1611.07308 - This foundational paper presents the Variational Graph Auto-Encoder (VGAE), an early and influential model for learning graph latent representations.
- MolGAN: An Implicit Generative Model for Small Molecular Graphs, Nicola De Cao, Thomas Kipf, 2018 ICML 2018 workshop on Theoretical Foundations and Applications of Deep Generative Models DOI: 10.48550/arXiv.1805.11973 - This paper introduces MolGAN, an implicit generative model using GANs for molecular graph generation, addressing challenges of discrete graph structures.
- A Survey on Graph Generative Models, Lin Cai, Junran Wu, Carl Yang, 2021 Transactions on Machine Learning Research (TMLR) (Transactions on Machine Learning Research) DOI: 10.48550/TMLR.2021.03756 - This survey provides a comprehensive overview of various graph generative models, categorizing approaches and discussing challenges and applications.
- Graph Representation Learning, William L. Hamilton, 2020 Synthesis Lectures on Artificial Intelligence and Machine Learning, Vol. 14 (Morgan & Claypool Publishers) DOI: 10.2200/S01045ED1V01Y202009AIM046 - Chapter 9 of this book provides a detailed introduction to graph generative models, including sequential, VAE, and GAN approaches.