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 Signal Processing Concepts
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- The Emerging Field of Signal Processing on Graphs: Extending High-Dimensional Data Analysis to Networks and Other Irregular Domains, David I. Shuman, Benjamin Ricaud, Pierre Vandergheynst, 2013 IEEE Signal Processing Magazine, Vol. 30 DOI: 10.1109/MSP.2012.2235192 - Foundational survey introducing the core concepts of Graph Signal Processing, including graph Laplacians, the Graph Fourier Transform, and spectral graph filtering.
- Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges, Michael M. Bronstein, Joan Bruna, Taco Cohen, Petar Veličković, 2021 (MIT Press) - A comprehensive and modern textbook that provides a unified theoretical framework for geometric deep learning, with extensive coverage of Graph Signal Processing as a foundation for Graph Neural Networks.
- Spectral Networks and Deep Locally Connected Networks on Graphs, Joan Bruna, Wojciech Zaremba, Arthur Szlam, Yann LeCun, 2014 ICLR 2014 Workshop DOI: 10.48550/arXiv.1312.6203 - Seminal work that introduces spectral graph convolutions, bridging Graph Signal Processing concepts with deep learning architectures on graphs.
- Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering, Michaël Defferrard, Xavier Bresson, Pierre Vandergheynst, 2016 Advances in Neural Information Processing Systems 29 (NeurIPS) - Introduces the use of Chebyshev polynomials to efficiently approximate graph convolutional filters, enabling localized and scalable spectral GNNs (ChebNet).