| Bridging collaborative filtering and semi-supervised learning: a neural approach for poi recommendation C Yang, L Bai, C Zhang, Q Yuan, J Han Proceedings of the 23rd ACM SIGKDD international conference on knowledge …, 2017 | 373 | 2017 |
| Adversarial attack and defense on graph data: A survey L Sun, Y Dou, C Yang, K Zhang, J Wang, SY Philip, L He, B Li IEEE Transactions on Knowledge and Data Engineering 35 (8), 7693-7711, 2022 | 371 | 2022 |
| Heterogeneous network representation learning: A unified framework with survey and benchmark C Yang, Y Xiao, Y Zhang, Y Sun, J Han IEEE Transactions on Knowledge and Data Engineering 34 (10), 4854-4873, 2020 | 361* | 2020 |
| Fedgraphnn: A federated learning system and benchmark for graph neural networks C He, K Balasubramanian, E Ceyani, C Yang, H Xie, L Sun, L He, L Yang, ... arXiv preprint arXiv:2104.07145, 2021 | 212 | 2021 |
| Federated graph classification over non-iid graphs H Xie, J Ma, L Xiong, C Yang Advances in neural information processing systems 34, 18839-18852, 2021 | 187 | 2021 |
| Subgraph federated learning with missing neighbor generation K Zhang, C Yang, X Li, L Sun, SM Yiu Advances in Neural Information Processing Systems 34, 6671-6682, 2021 | 185 | 2021 |
| Braingb: a benchmark for brain network analysis with graph neural networks H Cui, W Dai, Y Zhu, X Kan, AAC Gu, J Lukemire, L Zhan, L He, Y Guo, ... IEEE transactions on medical imaging 42 (2), 493-506, 2022 | 144 | 2022 |
| Brain network transformer X Kan, W Dai, H Cui, Z Zhang, Y Guo, C Yang Advances in Neural Information Processing Systems 35, 25586-25599, 2022 | 139 | 2022 |
| Transfer learning of graph neural networks with ego-graph information maximization Q Zhu, C Yang, Y Xu, H Wang, C Zhang, J Han Advances in Neural Information Processing Systems 34, 1766-1779, 2021 | 134 | 2021 |
| Conditional structure generation through graph variational generative adversarial nets C Yang, P Zhuang, W Shi, A Luu, P Li Advances in neural information processing systems 32, 2019 | 117 | 2019 |
| I know you'll be back: Interpretable new user clustering and churn prediction on a mobile social application C Yang, X Shi, L Jie, J Han Proceedings of the 24th ACM SIGKDD International Conference on Knowledge …, 2018 | 114 | 2018 |
| Domain specialization as the key to make large language models disruptive: A comprehensive survey C Ling, X Zhao, J Lu, C Deng, C Zheng, J Wang, T Chowdhury, Y Li, ... arXiv preprint arXiv:2305.18703, 2023 | 105 | 2023 |
| Fbnetgen: Task-aware gnn-based fmri analysis via functional brain network generation X Kan, H Cui, J Lukemire, Y Guo, C Yang International Conference on Medical Imaging with Deep Learning, 618-637, 2022 | 98 | 2022 |
| Interpretable graph neural networks for connectome-based brain disorder analysis H Cui, W Dai, Y Zhu, X Li, L He, C Yang International Conference on Medical Image Computing and Computer-Assisted …, 2022 | 96 | 2022 |
| On positional and structural node features for graph neural networks on non-attributed graphs H Cui, Z Lu, P Li, C Yang Proceedings of the 31st ACM International Conference on Information …, 2022 | 94 | 2022 |
| A survey on graph structure learning: Progress and opportunities Y Zhu, W Xu, J Zhang, Y Du, J Zhang, Q Liu, C Yang, S Wu arXiv preprint arXiv:2103.03036, 2021 | 88 | 2021 |
| When do gnns work: Understanding and improving neighborhood aggregation Y Xie, S Li, C Yang, RCW Wong, J Han IJCAI'20: Proceedings of the Twenty-Ninth International Joint Conference on …, 2020 | 78 | 2020 |
| Graph auto-encoder via neighborhood wasserstein reconstruction M Tang, C Yang, P Li arXiv preprint arXiv:2202.09025, 2022 | 69 | 2022 |
| Understanding structural vulnerability in graph convolutional networks L Chen, J Li, Q Peng, Y Liu, Z Zheng, C Yang arXiv preprint arXiv:2108.06280, 2021 | 69 | 2021 |
| mvn2vec: Preservation and collaboration in multi-view network embedding Y Shi, F Han, X He, X He, C Yang, J Luo, J Han arXiv preprint arXiv:1801.06597, 2018 | 68 | 2018 |
Hejie CuiStanford UniversityVerified email at stanford.edu