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PVLDB 2024 Tutorial: Efficient Training of Graph Neural Networks on Large graphs

This repository provides our PVLDB 2024 tutorial paper, slides, and a list of important works on the related topics as follows.

  1. NeutronOrch: Rethinking sample-based GNN training under CPU-GPU heterogeneous environments. arXiv:2311.13225 (2023).
  2. Company-as-tribe: Company financial risk assessment on tribe-style graph with hierarchical graph neural networks. In KDD. 2712–2720. 2022.
  3. DGCL: An efficient communication library for distributed GNN training. In EuroSys. 130–144. 2021.
  4. DSP: Efficient GNN training with multiple GPUs. In PPoPP. 392–404. 2023.
  5. Efficient scaling of dynamic graph neural networks. In SC. 1–15. 2021.
  6. Exgc: Bridging efficiency and explainability in graph condensation. arXiv preprint arXiv:2402.05962 (2024).
  7. Optimizing DNN computation graph using graph substitutions. VLDB 13, 12 (2020), 2734–2746. paper code
  8. STile: Searching hybrid sparse formats for sparse deep learning operators automatically. Proceedings of the ACM on Management of Data 2, 1 (2024), 1–26. paper code
  9. P3: Distributed deep graph learning at scale. In OSDI. 551–568. 2021.
  10. Graph neural networks for recommender system. In WSDM. 1623–1625. 2022.
  11. ETC: Efficient training of temporal graph neural networks over large-scale dynamic graphs. VLDB 17, 5 (2024), 1060–1072. paper code
  12. SIMPLE: Efficient temporal graph neural network training at scale with dynamic data placement. Proceedings of the ACM on Management of Data 2, 3 (2024), 1–25. paper code
  13. Traversing large graphs on GPUs with unified memory. VLDB 13, 7 (2020), 1119–1133.
  14. Open graph benchmark: Datasets for machine learning on graphs. NeurIPS 33 (2020), 22118–22133.
  15. Opinion leaders for information diffusion using graph neural network in online social networks. TWEB 17, 2 (2023), 1–37.
  16. Accelerating graph sampling for graph machine learning using GPUs. In EuroSys. 311–326. 2021.
  17. Improving the accuracy, scalability, and performance of graph neural networks with roc. MLSys 2 (2020), 187–198.
  18. Redundancy-free computation for graph neural networks. In KDD. 997–1005. 2020.
  19. Pre-training on large-scale heterogeneous graph. In KDD. 756–766. 2021.
  20. Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 (2016).
  21. Cache-based gnn system for dynamic graphs. In CIKM. 937–946. 2021.
  22. Orca: Scalable temporal graph neural network training with theoretical guarantees. Proc. ACM Manag. Data 1, 1 (2023), 52:1–52:27. paper code
  23. Zebra: When temporal graph neural networks meet temporal personalized PageRank. VLDB 16, 6 (2023), 1332–1345. paper code
  24. Cc-gnn: A community and contraction-based graph neural network. In ICDM. IEEE, 231–240. 2022.
  25. DAHA: Accelerating GNN training with Data and Hardware Aware Execution Planning. VLDB 17, 6 (2024), 1364–1376.
  26. Pagraph: Scaling GNN training on large graphs via computation-aware caching. In SoCC. 401–415. 2020.
  27. SANCUS: Staleness-aware communication-avoiding full-graph decentralized training in large-scale graph neural networks. VLDB 15, 9 (2022), 1937–1950. paper code
  28. NeutronStar: Distributed GNN training with hybrid dependency management. In SIGMOD. 1301–1315. 2022.
  29. GNNAdvisor: An adaptive and efficient runtime system for GNN acceleration on GPUs. In OSDI. 515–531. 2021.
  30. Kernel ridge regression-based graph dataset distillation. In KDD. 2850–2861. 2023.
  31. Large-scale graph neural networks: The Past and New Frontiers. In KDD. 5835– 5836. 2023.
  32. GNNLab: a factored system for sample-based GNN training over GPUs. In EuroSys. 417–434. 2022.
  33. Feature-oriented sampling for fast and scalable GNN training. In ICDM. IEEE, 723–732. 2022. paper code
  34. DUCATI: A dual-cache training system for graph neural networks on giant graphs with the GPU. Proc. ACM Manag. Data 1, 2 (2023), 166:1–166:24. 2023. paper code
  35. NSCaching: simple and efficient negative sampling for knowledge graph embedding. In ICDE. IEEE, 614–625. 2019.
  36. Learning on large-scale text-attributed graphs via variational inference. arXiv preprint arXiv:2210.14709 (2022). 2022.
  37. Structure-free graph condensation: From large-scale graphs to condensed graph-free data. NeurIPS 36 (2024). 2024.

Cite

If you find this useful for your work, please consider citing it as follows:

@article{vldb24shen,
  title = {Efficient Training of Graph Neural Networks on Large Graphs},
  author = {Shen, Yanyan and Chen, Lei and Fang, Jingzhi and Zhang, Xin and Gao, Shihong and Yin, Hongbo},
  journal = {Proc. {VLDB} Endow.},
  year = {2024},
  doi  = {10.14778/3685800.3685844},
}