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
The Message Passing Framework Revisited
Was this section helpful?
- Relational Inductive Biases, Deep Learning, and Graph Networks, Peter W. Battaglia, Jessica B. Hamrick, Victor Bapst, Alvaro Sanchez-Gonzalez, Vinicius Zambaldi, Mateusz Malinowski, Andrea Tacchetti, David Raposo, Adam Santoro, Ryan Faulkner, Caglar Gulcehre, Francis Song, Andrew Ballard, Justin Gilmer, George Dahl, Ashish Vaswani, Kelsey Allen, Charles Nash, Victoria Langston, Chris Dyer, Nicolas Heess, Daan Wierstra, Pushmeet Kohli, Matt Botvinick, Oriol Vinyals, Yujia Li, Razvan Pascanu, Thomas P. Lillicrap, 2018 arXiv preprint arXiv:1806.01261 DOI: 10.48550/arXiv.1806.01261 - This paper defines the unified 'Graph Networks' framework, which encompasses message passing and formalizes its components.
- How Powerful are Graph Neural Networks?, Keyulu Xu, Weihua Hu, Jure Leskovec, Yoshua Bengio, 2019 International Conference on Learning Representations (ICLR) DOI: 10.48550/arXiv.1810.00826 - Introduces the Graph Isomorphism Network (GIN) and formally connects the expressive capacity of GNNs to the 1-Weisfeiler-Lehman test.
- Weisfeiler and Lehman Go Neural: Higher-order Graph Neural Networks, Christopher Morris, Martin Ritzert, Matthias Fey, Stefan Faber, Hongzhi Wen, William Hamilton, Laurent Schonsberg, Michiharu Akimoto, Martin Gütlein, Daniel Kuehn, Karsten Borgwardt, 2019 Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 33 (American Association for Artificial Intelligence (AAAI)) DOI: 10.1609/aaai.v33i01.33013990 - Presents a theoretical framework for analyzing the expressive capacity of GNNs concerning the Weisfeiler-Lehman test, including higher-order variants.
© 2025 ApX Machine LearningEngineered with