| Rethinking Graph Neural Networks for Anomaly Detection J Tang, J Li, Z Gao, J Li ICML, 2022 | 163 | 2022 |
| Semi-supervised graph classification: A hierarchical graph perspective J Li, Y Rong, H Cheng, H Meng, W Huang, J Huang The World Wide Web Conference, 972-982, 2019 | 153 | 2019 |
| Predicting path failure in time-evolving graphs J Li, Z Han, H Cheng, J Su, P Wang, J Zhang, L Pan Proceedings of the 25th ACM SIGKDD international conference on knowledge …, 2019 | 141 | 2019 |
| All in One: Multi-Task Prompting for Graph Neural Networks X Sun, H Cheng, J Li, B Liu, J Guan KDD 2023, 2023 | 101* | 2023 |
| Adversarial attack on community detection by hiding individuals J Li, H Zhang, Z Han, Y Rong, H Cheng, J Huang Proceedings of The Web Conference 2020, 917-927, 2020 | 92 | 2020 |
| Hierarchical graph learning for protein–protein interaction Z Gao, C Jiang, J Zhang, X Jiang, L Li, P Zhao, H Yang, Y Huang, J Li Nature Communications 14 (1), 1093, 2023 | 73 | 2023 |
| TATC: predicting Alzheimer's disease with actigraphy data J Li, Y Rong, H Meng, Z Lu, T Kwok, H Cheng Proceedings of the 24th ACM SIGKDD International Conference on Knowledge …, 2018 | 55 | 2018 |
| Dirichlet graph variational autoencoder J Li, J Yu, J Li, H Zhang, K Zhao, Y Rong, H Cheng, J Huang NeurIPS 33, 5274--5283, 2020 | 46 | 2020 |
| A survey of graph meets large language model: Progress and future directions Y Li, Z Li, P Wang, J Li, X Sun, H Cheng, JX Yu IJCAI 2024, 2023 | 45* | 2023 |
| Large Language Models Meet Harry Potter: A Bilingual Dataset for Aligning Dialogue Agents with Characters N Chen, Y Wang, H Jiang, D Cai, Y Li, Z Chen, L Wang, J Li EMNLP Findings, 2023 | 35* | 2023 |
| Self-supervised hypergraph representation learning for sociological analysis X Sun, H Cheng, B Liu, J Li, H Chen, G Xu, H Yin IEEE Transactions on Knowledge and Data Engineering, 2023 | 34 | 2023 |
| Graph prompt learning: A comprehensive survey and beyond X Sun, J Zhang, X Wu, H Cheng, Y Xiong, J Li arXiv preprint arXiv:2311.16534, 2023 | 29 | 2023 |
| GADBench: Revisiting and Benchmarking Supervised Graph Anomaly Detection J Tang, F Hua, Z Gao, P Zhao, J Li NeurIPS 2023, 2023 | 28 | 2023 |
| Deconvolutional Networks on Graph Data J Li, J Li, Y Liu, J Yu, Y Li, H Cheng NeurIPS 34, 21019--21030, 2021 | 24 | 2021 |
| Semi-supervised hierarchical graph classification J Li, Y Huang, H Chang, Y Rong IEEE Transactions on Pattern Analysis and Machine Intelligence 45 (5), 6265-6276, 2022 | 23 | 2022 |
| A Convergent Single-Loop Algorithm for Relaxation of Gromov-Wasserstein in Graph Data J Li, J Tang, L Kong, H Liu, J Li, AMC So, J Blanchet ICLR 2023, 2023 | 17* | 2023 |
| Wiener Graph Deconvolutional Network Improves Graph Self-Supervised Learning J Cheng, M Li, J Li, F Tsung AAAI 2023, 2023 | 16 | 2023 |
| Breaking language barriers in multilingual mathematical reasoning: Insights and observations N Chen, Z Zheng, N Wu, L Shou, M Gong, Y Song, D Zhang, J Li arXiv preprint arXiv:2310.20246, 2023 | 15 | 2023 |
| Warpformer: A Multi-scale Modeling Approach for Irregular Clinical Time Series J Zhang, S Zheng, W Cao, J Bian, J Li KDD 2023, 2023 | 14 | 2023 |
| A Fused Gromov-Wasserstein Framework for Unsupervised Knowledge Graph Entity Alignment J Tang, K Zhao, J Li ACL 2023 Findings, 2023 | 14 | 2023 |
Hong ChengProfessor, The Chinese University of Hong KongVerified email at se.cuhk.edu.hk
