Kaize Ding

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Huan LiuRegents Professor, Arizona State University在 asu.edu 的電子郵件地址已通過驗證
Jundong LiAssistant Professor, University of Virginia在 virginia.edu 的電子郵件地址已通過驗證
Jianling WangResearch Scientist, Google DeepMind在 tamu.edu 的電子郵件地址已通過驗證
Kai ShuAssistant Professor of Computer Science, Emory University在 emory.edu 的電子郵件地址已通過驗證
James CaverleeProfessor, Computer Science and Engineering, Texas A&M University在 cse.tamu.edu 的電子郵件地址已通過驗證
Hanghang TongUniversity of Illinois at Urbana-Champaign在 illinois.edu 的電子郵件地址已通過驗證
Shirui PanFull Professor, ARC Future Fellow, FQA, Director of TrustAGI Lab, Griffith University在 griffith.edu.au 的電子郵件地址已通過驗證
Philip S. YuProfessor of Computer Science, University of Illinons at Chicago在 cs.uic.edu 的電子郵件地址已通過驗證
Ruocheng GuoIntuit AI在 bytedance.com 的電子郵件地址已通過驗證
Yixin LiuResearch Fellow, School of ICT, Griffith University在 griffith.edu.au 的電子郵件地址已通過驗證
Zhen TanPh.D. at Arizona State University在 asu.edu 的電子郵件地址已通過驗證
Yichuan LiWorcester Polytechnic Institute在 wpi.edu 的電子郵件地址已通過驗證
Yue ZhaoAssistant Professor of Computer Science, University of Southern California在 usc.edu 的電子郵件地址已通過驗證
Song WangPh.D. Student, University of Virginia在 virginia.edu 的電子郵件地址已通過驗證
Zhe XuUniversity of Illinois Urbana-Champaign在 illinois.edu 的電子郵件地址已通過驗證
Chuxu ZhangAssociate Professor of CSE, University of Connecticut (UConn)在 uconn.edu 的電子郵件地址已通過驗證
Chenghao LiuSalesforce Research Asia在 salesforce.com 的電子郵件地址已通過驗證
Yingtong DouResearch Scientist, Visa Inc.在 visa.com 的電子郵件地址已通過驗證
Nitesh V Chawla, Fellow: ACM, IEEE,...Frank Freimann Professor of CSE.,Director, Lucy Family Institute for Data & Soc, Univ. of Notre Dame在 nd.edu 的電子郵件地址已通過驗證
Yushun DongAssistant Professor, Department of Computer Science, Florida State University在 fsu.edu 的電子郵件地址已通過驗證

追蹤

Kaize Ding

Assistant Professor of Stats & Data Science, Northwestern University

在 northwestern.edu 的電子郵件地址已通過驗證 - 首頁

Reliable Machine Learning Data-Efficient Learning Anomaly/OOD Detection LLMs and GNNs


標題按引用次數排序按年份排序按標題排序	引用次數引用次數	年份
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	541	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	300	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	275	2022
Be more with less: Hypergraph attention networks for inductive text classification K Ding, J Wang, J Li, D Li, H Liu EMNLP 2020, 2020	265	2020
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	231	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	187	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	170	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	143	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	127*	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	115	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	105	2020
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	87	2020
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	78	2021
GOOD-D: On Unsupervised Graph Out-Of-Distribution Detection Y Liu, K Ding, H Liu, S Pan WSDM 2023, 2022	71	2022
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	71	2022
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	58	2024
Sequential recommendation for cold-start users with meta transitional learning J Wang, K Ding, J Caverlee Proceedings of the 44th International ACM SIGIR Conference on Research and …, 2021	58	2021
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	55	2022
Few-shot learning on graphs C Zhang, K Ding, J Li, X Zhang, Y Ye, NV Chawla, H Liu arXiv preprint arXiv:2203.09308, 2022	54	2022
Meta Propagation Networks for Graph Few-shot Semi-supervised Learning K Ding, J Wang, J Caverlee, H Liu AAAI 2022, 2022	53	2022

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