Reading Roadmap for the Beginners
0, Tutorials, Books
- Graph Neural Networks | SpringerLink
- Jiliang Tang: https://cse.msu.edu/~mayao4/dlg_book/
- AAAI 19 Graph Representation Learning: part-0, part-1, part-2, part-3
- AAAI 20: Graph Neural Networks: Models and Applications
1, Basic GNN model:
- GCN: the most basic model
- Understand GCN in spatial domain: Deeper Insights into Graph Convolutional Networks for Semi-Supervised Learning
- Understand GCN in spatial domain: Label Efficient Semi-Supervised Learning via Graph Filtering:
2, Fundamental of Graph Signal Processing (GSP)
- The Emerging Field of Signal Processing on Graphs [Jouranl Version][Code]
- Vertex-Frequency Analysis on Graphs
- A Tutorial on Spectral Clustering
- Discrete Signal Processing on Graphs
- Design of Graph Filters and Filterbanks
- Discrete Signal Processing on Graphs
- Discrete Signal Processing on Graphs: Frequency Analysis
- Graph Signal Processing: Overview, Challenges, and Applications
- Discrete Signal Processing on Graphs: Sampling Theory
- Signals on Graphs: Uncertainty Principle and Sampling
3, Surveys
- Geometric Deep Learning
- Graph Neural Networks: A Review of Methods and Applications
- A Comprehensive Survey on Graph Neural Networks
4, Paper Collection
5, Coding Framework
Benchmarks
- Benchmarking Graph Neural Networks
- Stanford Large Network Dataset Collection
- Open Graph Benchmark
- Graphs | Papers With Code
References
Graph Theory
- Graph Theory - Reinhard Diestel
- Graph Theory - J.A. Bondy, U.S.R. Murty
- On the Graph Fourier Transform for Directed Graphs
Explainable Graph
- GNNExplainer: Generating Explanations for Graph Neural Networks
- Explainability Techniques for Graph Convolutional Networks
- ExplaiNE: An Approach for Explaining Network Embedding-based Link Predictions
- NIPS 19: Using Embeddings to Correct for Unobserved Confounding in Networks