This repository is created to keep track of my learning journey in the field of graph machine learning. Here, you can find my study notes, code implementations, and other useful resources that I collected during my learning process.
- The Graph Neural Network Model, TNN-2009
- Efficient graphlet kernels for large graph comparison, AISTATS-2009
- Weisfeiler-Lehman Graph Kernels, JMLR-2011
- DeepWalk: online learning of social representations, KDD-2014
- Learning Convolutional Neural Networks for Graphs, ICML
- node2vec: Scalable Feature Learning for Networks, KDD
- Variational Graph Auto-Encoders, NIPS
- Semi-Supervised Classification with Graph Convolutional Networks, ICLR
- Inductive Representation Learning on Large Graphs, NIPS
- Dynamic Edge-Conditioned Filters in Convolutional Neural Networks on Graphs, CVPR
- graph2vec: Learning Distributed Representations of Graphs, MLG Workshop
- Neural Message Passing for Quantum Chemistry, ICML
- Semi-Supervised Classification with Graph Convolutional Networks, ICLR
- Graph Attention Networks, ICLR
- An End-to-End Deep Learning Architecture for Graph Classification, AAAI
- Graph Attention Networks, ICLR
- Hierarchical graph representation learning with differentiable pooling, NIPS
- Link prediction based on graph neural networks, NIPS
- How Powerful are Graph Neural Networks?, ICLR
- Weisfeiler and Leman Go Neural: Higher-order Graph Neural Networks, AAAI
- A Simple Yet Effective Baseline For Non-Attributed Graph Classification, ICLR workshop on Representation learning on graphs and manifolds
- DEMO-Net: Degree-specific Graph Neural Networks for Node and Graph Classification, KDD
- Graph U-Nets, ICML
- Provably Powerful Graph Networks, NIPS
- Self-Attention Graph Pooling, ICML
- Weisfeiler and leman go neural: higher-order graph neural networks, AAAI
- Simplifying Graph Convolutional Networks, ICML
- A Survey on Graph Kernels, ML
- Distance encoding: design provably more powerful neural networks for graph representation learning, NIPS
- Haar Graph Pooling, ICML
- Rethinking pooling in graph neural networks, NIPS
- A Generalization of Transformer Networks to Graphs, AAAI
- Graph Contrastive Learning with Augmentations, NIPS
- A Comprehensive Survey On Graph Neural Networks, TNNLS
- Bridging the Gap between Spatial and Spectral Domains: A Survey on Graph Neural Networks, ACM Comput. Surv.
- Do Transformers Really Perform Badly for Graph Representation?, NIPS
- Improving Graph Neural Network Expressivity via Subgraph Isomorphism Counting, ICLR
- Nested Graph Neural Networks, NIPS
- Simple Spectral Graph Convolution, ICLR
- Understanding Pooling in Graph Neural Networks, TNNLS
- Wasserstein Embedding for Graph Learning, ICLR
- A New Perspective on "How Graph Neural Networks Go Beyond Weisfeiler-Lehman?", ICLR
- Equivariant Subgraph Aggregation Networks, ICLR
- From Stars to Subgraphs: Uplifting Any GNN with Local Structure Awareness, ICLR
- How Powerful are K-hop Message Passing Graph Neural Networks, NIPS
- How Powerful are Spectral Graph Neural Networks, ICML
- Ordered Subgraph Aggregation Networks, NIPS
- Structural Landmarking and Interaction Modelling: A “SLIM” Network for Graph Classification, AAAI
- Topological Graph Neural Networks, ICLR
- Universal Graph Transformer Self-Attention Networks, WWW
- Survey on Graph Classification (图分类研究综述), Journal of Software
- A Survey on Spectral Graph Neural Networks, arxiv
- Are More Layers Beneficial to Graph Transformers?, ICLR
- Boosting the Cycle Counting Power of Graph Neural Networks with $I^2$-GNNs, ICLR
- Cycle to Clique (Cy2C) Graph Neural Network: A Sight to See beyond Neighborhood Aggregation, ICLR
- Graph Neural Networks are Inherently Good Generalizers: Insights by Bridging GNNs and MLPs, ICLR
- Graph Pooling for Graph Neural Networks: Progress, Challenges, and Opportunities, IJCAI
- MLPInit: Embarrassingly Simple GNN Training Acceleration with MLP Initialization, ICLR
- $\mathcal{N}$-WL：A New Hierarchy of Expressivity for Graph Neural Networks, ICLR
- Rethinking the Expressive Power of GNNs via Graph Biconnectivity, ICLR
- Empowering Graph Representation Learning with Test-Time Graph Transformation, ICLR
- All in One: Multi-task Prompting for Graph Neural Networks, KDD
- [Book] Graph Representation Learning
- [Book] Complex Network Books
- [Book] Dive into Deep Learning, 动手学深度学习
- [Python Framework] DGL
- [Python Framework] NetworkX
- [Book] Graph Neural Networks: Foundations, Frontiers, and Applications
- [Course] CS224W: Machine Learning with Graphs
- [Book] A brief introduction to Spectral Graph Theory
If you find any errors or have suggestions for improving this repository, please feel free to create an issue or submit a pull request.