| Deep anomaly detection on attributed networks K Ding, J Li, R Bhanushali, H Liu Proceedings of the 2019 SIAM international conference on data mining, 594-602, 2019 | 733 | 2019 |
| Next-item recommendation with sequential hypergraphs J Wang, K Ding, L Hong, H Liu, J Caverlee Proceedings of the 43rd international ACM SIGIR conference on research and …, 2020 | 355 | 2020 |
| Be more with less: Hypergraph attention networks for inductive text classification K Ding, J Wang, J Li, D Li, H Liu EMNLP 2020, 2020 | 352 | 2020 |
| Data augmentation for deep graph learning: A survey K Ding, Z Xu, H Tong, H Liu ACM SIGKDD Explorations Newsletter 24 (2), 61-77, 2022 | 348 | 2022 |
| Combating disinformation in a social media age K Shu, A Bhattacharjee, F Alatawi, TH Nazer, K Ding, M Karami, H Liu Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 10 (6 …, 2020 | 321 | 2020 |
| Interactive anomaly detection on attributed networks K Ding, J Li, H Liu Proceedings of the twelfth ACM international conference on web search and …, 2019 | 222 | 2019 |
| Graph prototypical networks for few-shot learning on attributed networks K Ding, J Wang, J Li, K Shu, C Liu, H Liu Proceedings of the 29th ACM international conference on information …, 2020 | 215 | 2020 |
| Few-shot network anomaly detection via cross-network meta-learning K Ding, Q Zhou, H Tong, H Liu Proceedings of the web conference 2021, 2448-2456, 2021 | 191 | 2021 |
| BOND: Benchmarking Unsupervised Outlier Node Detection on Static Attributed Graphs K Liu, Y Dou, Y Zhao, X Ding, X Hu, R Zhang, K Ding, C Chen, H Peng, ... arXiv preprint arXiv:2206.10071, 2022 | 179* | 2022 |
| Session-based recommendation with hypergraph attention networks J Wang, K Ding, Z Zhu, J Caverlee Proceedings of the 2021 SIAM international conference on data mining (SDM …, 2021 | 150 | 2021 |
| Inductive anomaly detection on attributed networks K Ding, J Li, N Agarwal, H Liu Proceedings of the Twenty-Ninth International Conference on International …, 2020 | 132 | 2020 |
| GOOD-D: On Unsupervised Graph Out-Of-Distribution Detection Y Liu, K Ding, H Liu, S Pan WSDM 2023, 2022 | 108 | 2022 |
| Adagnn: Graph neural networks with adaptive frequency response filter Y Dong, K Ding, B Jalaian, S Ji, J Li Proceedings of the 30th ACM international conference on information …, 2021 | 108 | 2021 |
| Graph few-shot learning with attribute matching N Wang, M Luo, K Ding, L Zhang, J Li, Q Zheng Proceedings of the 29th ACM International Conference on Information …, 2020 | 104 | 2020 |
| Towards Self-Interpretable Graph-Level Anomaly Detection Y Liu, K Ding, Q Lu, F Li, LY Zhang, S Pan NeurIPS 2023, 2023 | 100 | 2023 |
| Exploring concept depth: How large language models acquire knowledge and concept at different layers? M Jin, Q Yu, J Huang, Q Zeng, Z Wang, W Hua, H Zhao, K Mei, Y Meng, ... Proceedings of the 31st international conference on computational …, 2025 | 90* | 2025 |
| Pygod: A python library for graph outlier detection K Liu, Y Dou, X Ding, X Hu, R Zhang, H Peng, L Sun, PS Yu Journal of Machine Learning Research 25 (141), 1-9, 2024 | 84 | 2024 |
| Virtual node tuning for few-shot node classification Z Tan, R Guo, K Ding, H Liu Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and …, 2023 | 80 | 2023 |
| Graph few-shot class-incremental learning Z Tan, K Ding, R Guo, H Liu Proceedings of the fifteenth ACM international conference on web search and …, 2022 | 80 | 2022 |
| Task-adaptive few-shot node classification S Wang, K Ding, C Zhang, C Chen, J Li Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and …, 2022 | 75 | 2022 |
Huan LiuRegents Professor, Arizona State UniversityVerified email at asu.edu
