Top AI Repos — open-source AI, indexed and scored
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
| Date | Stars |
|---|---|
| 2026-07-31 | 268 |
| 2026-08-05 | 268 |
| 2026-08-06 | 268 |
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[TOC] # Introduction Graph neural network, as a powerful graph representation learning method, has been widely used in diverse scenarios, such as NLP, CV, and recommender systems. As far as I can see, graph mining is highly related to recommender systems. Recommend one item to one user actually is the link prediction on the user-item graph. This repository mainly consists of three parts: - **Graph Neural Network** - **GNN based Recommendation** - **GNN related Resources** - Materials & Paper & Code - **Dataset for GNN or Recommendation** We also have an Wechat Official Account, providing some materials about GNN and Recommendation.  You're most welcome to join us with any contributions for GNN and Recommendation! Here is the template for contributors: ``` [ID] Authors. **Paper_Name**. Conference&Year. [Paper](Paper_Link) ``` A simple example for template: ``` 1. Long, Qingqing and Jin, Yilun and Song, Guojie and Li, Yi and Lin, Wei. **Graph Structural-topic Neural Network**. KDD 2020. [paper](https://arxiv.org/abs/2006.14278) ``` # Graph Neural Network 1. Giannis Nikolentzos and Michalis Vazirgiannis.**Random Walk Graph Neural Networks**. NeurIPS 2020.[paper](https://www.lix.polytechnique.fr/~nikolentzos/files/rw_gnns_neurips20) 2. Nicolas Keriven and Alberto Bietti and Samuel Vaiter. **Convergence and Stability of Graph Convolutional Networks on Large Random Graphs**. NeurIPS 2020. [paper](https://arxiv.org/abs/2006.01868) 3. Nikolaos Karalias and Andreas Loukas. **Erdos Goes Neural: an Unsupervised Learning Framework for Combinatorial Optimization on Graphs**. NeurIPS 2020. [paper](https://arxiv.org/abs/2006.10643) 4. Xiang Zhang and Marinka Zitnik. **GNNGuard: Defending Graph Neural Networks against Adversarial Attacks**. NeurIPS 2020. [paper](https://arxiv.org/abs/2006.08149) 5. Zheng Ma and Junyu Xuan and Yu Guang Wang and Ming Li and Pietro Lio. **Path Integral Based Convolution and Pooling for Graph Neural Networks** NeurIPS 2020. [paper](https://arxiv.org/abs/2006.16811) <details> <summary> more </summary> 6. Daniel D. Johnson and Hugo Larochelle and Daniel Tarlow. **Learning Graph Structure With A Finite-State Automaton Layer**. NeurIPS 2020. [paper](https://arxiv.org/abs/2007.04929) 7. Vitaly Kurin and Saad Godil and Shimon Whiteson and Bryan Catanzaro. **Improving SAT Solver Heuristics with Graph Networks and Reinforcement Learning**. NeurIPS 2020. [paper](https://arxiv.org/abs/1909.11830) 8. Zhiwei Deng and Karthik Narasimhan and Olga Russakovsky. **Evolving Graphical Planner: Contextual Global Planning for Vision-and-Language Navigation** NeurIPS 2020. [paper](https://arxiv.org/abs/2007.05655) 9. Long, Qingqing and Jin, Yilun and Song, Guojie and Li, Yi and Lin, Wei. **Graph Structural-topic Neural Network**. KDD 2020. [paper](https://arxiv.org/abs/2006.14278) 10. Zang, Chengxi and Wang, Fei. **Neural Dynamics on Complex Networks** KDD2020. [paper](https://arxiv.org/abs/1908.06491) 11. Ganqu Cui, Jie Zhou, Cheng Yang, Zhiyuan Liu. Adaptive Graph Encoder for Attributed Graph Embedding KDD 2020. [paper](https://arxiv.org/pdf/2007.01594.pdf) 12. Dynamic Deep Neural Networks: Optimizing Accuracy-Efficiency Trade-offs by Selective Execution. AAAI 2018 13. Dynamic Network Embedding by Modeling Triadic Closure Process. AAAI 2018 14. DepthLGP: Learning Embeddings of Out-of-Sample Nodes in Dynamic Networks. AAAI 2018 15. A Generative Model for Dynamic Networks with Applications. AAAI 2019 16. Communication-optimal distributed dynamic graph clustering. AAAI 2019 17. EvolveGCN: Evolving Graph Convolutional Networks for Dynamic Graphs. AAAI 2020 18. Dynamic Network Pruning with Interpretable Layerwise Channel Selection. AAAI 2020 19. DyRep: Learning Representations over Dynamic Graphs. ICLR 2019 20. Dynamic Graph Representation Learning via Self-Attention Networks. ICLR 2019 21. The Logical Expressiveness of Graph Neural Networks. ICLR 2020 22. Fast and Accurate Random Walk wit
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Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:25c758a5bf659973, llm:topics: graph, graph-neural-networks, recommendation-system; description: Graph Neural Network (awesome list for GNN recommendation systems)
matched fp:25c758a5bf659973, llm:topics: graph, graph-neural-networks, recommendation-system; description: Graph Neural Network (awesome list for GNN recommendation systems)
matched fp:25c758a5bf659973, llm:topics: graph, graph-neural-networks, recommendation-system; description: Graph Neural Network (awesome list for GNN recommendation systems)