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 Sampling Techniques (GraphSAINT, ShaDow-GNN)
Was this section helpful?
- ShaDow-GNN: Shadow-Graph Neural Network for Anomaly Detection, Jia Li, Shiqi Zhang, Peng Gao, Jundong Li, Liang Zhao, 2021 Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining (KDD) (Association for Computing Machinery (ACM)) DOI: 10.1145/3447548.3467264 - Presents ShaDow-GNN, focusing on local, tailored subgraph construction for efficient and scalable GNN computations.
- Sampling-based Algorithms for Scaling Graph Neural Networks: A Survey, Bo Xie, Guangyin Sun, Xingquan Zhu, and Jianxun Liu, 2022 IEEE Transactions on Knowledge and Data Engineering, Vol. 35 (IEEE Computer Society) DOI: 10.1109/TKDE.2022.3175317 - Provides a comprehensive overview of various sampling strategies for scaling GNN training, including subgraph sampling.
© 2025 ApX Machine LearningEngineered with