Awesome-HGNNs

A list for Heterogeneous GNNs and related works.

Pre-computation-based HGNNs

Pre-computation-based HGNNs are usually scalable and efficient.

• RpHGNN: Jun Hu, Bryan Hooi, and Bingsheng He. "Efficient heterogeneous graph learning via random projection." arXiv preprint arXiv:2310.14481 (2023). [paper][code]
• SeHGNN: Xiaocheng Yang, Mingyu Yan, Shirui Pan, Xiaochun Ye, and Dongrui Fan. "Simple and efficient heterogeneous graph neural network." In Proceedings of the AAAI conference on artificial intelligence, vol. 37, no. 9, pp. 10816-10824. 2023. [paper][code]
• NARS: Lingfan Yu, Jiajun Shen, Jinyang Li, and Adam Lerer. "Scalable graph neural networks for heterogeneous graphs." arXiv preprint arXiv:2011.09679 (2020). [paper][code]

End-to-End HGNNs

• HINormer: Qiheng Mao, Zemin Liu, Chenghao Liu, and Jianling Sun. "HINormer: Representation Learning On Heterogeneous Information Networks with Graph Transformer." In Proceedings of the ACM Web Conference 2023, pp. 599-610. 2023. [paper]
• Simple-HGN: Qingsong Lv, Ming Ding, Qiang Liu, Yuxiang Chen, Wenzheng Feng, Siming He, Chang Zhou, Jianguo Jiang, Yuxiao Dong, and Jie Tang. "Are we really making much progress? revisiting, benchmarking and refining heterogeneous graph neural networks." In Proceedings of the 27th ACM SIGKDD conference on knowledge discovery & data mining, pp. 1150-1160. 2021. [paper]
• HGT: Ziniu Hu, Yuxiao Dong, Kuansan Wang, and Yizhou Sun. "Heterogeneous graph transformer." In Proceedings of the web conference 2020, pp. 2704-2710. 2020. [paper]
• HetSANN: Huiting Hong, Hantao Guo, Yucheng Lin, Xiaoqing Yang, Zang Li, and Jieping Ye. "An attention-based graph neural network for heterogeneous structural learning." In Proceedings of the AAAI conference on artificial intelligence, vol. 34, no. 04, pp. 4132-4139. 2020. [paper]
• MAGNN: Xinyu Fu, Jiani Zhang, Ziqiao Meng, and Irwin King. "Magnn: Metapath aggregated graph neural network for heterogeneous graph embedding." In Proceedings of the web conference 2020, pp. 2331-2341. 2020. [paper]
• HetGNN: Chuxu Zhang, Dongjin Song, Chao Huang, Ananthram Swami, and Nitesh V. Chawla. "Heterogeneous graph neural network." In Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining, pp. 793-803. 2019. [paper]
• RSHN: Shichao Zhu, Chuan Zhou, Shirui Pan, Xingquan Zhu, and Bin Wang. "Relation structure-aware heterogeneous graph neural network." In 2019 IEEE international conference on data mining (ICDM), pp. 1534-1539. IEEE, 2019. [paper]
• GTN: Seongjun Yun, Minbyul Jeong, Raehyun Kim, Jaewoo Kang, and Hyunwoo J. Kim. "Graph transformer networks." Advances in neural information processing systems 32 (2019). [paper]
• HAN: Xiao Wang, Houye Ji, Chuan Shi, Bai Wang, Yanfang Ye, Peng Cui, and Philip S. Yu. "Heterogeneous graph attention network." In The world wide web conference, pp. 2022-2032. 2019. [paper]
• RGCN: Michael Schlichtkrull, Thomas N. Kipf, Peter Bloem, Rianne Van Den Berg, Ivan Titov, and Max Welling. "Modeling relational data with graph convolutional networks." In The semantic web: 15th international conference, ESWC 2018, Heraklion, Crete, Greece, June 3–7, 2018, proceedings 15, pp. 593-607. Springer International Publishing, 2018. [paper]

Datasets

• HGB (DBLP, IMDB, ACM, Freebase): [paper] [download]
• ogb-mag: [paper][leaderboard][download]