Kaize Ding 个人学术档案 - 学术资源搜索

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引用	2529	2529
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i10 指数	36	36

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Huan LiuRegents Professor, Arizona State University在 asu.edu 的电子邮件经过验证
Jundong LiAssistant Professor, University of Virginia在 virginia.edu 的电子邮件经过验证
Jianling WangResearch Scientist, Google DeepMind在 tamu.edu 的电子邮件经过验证
Kai ShuAssistant Professor of Computer Science, Illinois Institute of Technology在 iit.edu 的电子邮件经过验证
James CaverleeProfessor, Computer Science and Engineering, Texas A&M University在 cse.tamu.edu 的电子邮件经过验证
Hanghang TongUniversity of Illinois at Urbana-Champaign在 illinois.edu 的电子邮件经过验证
Ruocheng GuoByteDance Research, London在 bytedance.com 的电子邮件经过验证
Zhen TanArizona State University在 asu.edu 的电子邮件经过验证
Zhe XuUniversity of Illinois Urbana-Champaign在 illinois.edu 的电子邮件经过验证
Shirui PanFull Professor, ARC Future Fellow, FQA, Director of TrustAGI Lab, Griffith University在 griffith.edu.au 的电子邮件经过验证
Yichuan LiWorcester Polytechnic Institute在 wpi.edu 的电子邮件经过验证
Song WangPhD. Student, University of Virginia在 virginia.edu 的电子邮件经过验证
Yixin LiuPh.D. Candidate, Monash University在 monash.edu 的电子邮件经过验证
Yingtong DouResearch Scientist, Visa Inc.在 visa.com 的电子邮件经过验证
Philip S. YuProfessor of Computer Science, University of Illinons at Chicago在 cs.uic.edu 的电子邮件经过验证
Chuxu ZhangAssistant Professor, Computer Science, Brandeis University在 brandeis.edu 的电子邮件经过验证
Ziwei ZhuAssistant Professor at George Mason University在 gmu.edu 的电子邮件经过验证
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 的电子邮件经过验证
Yue ZhaoAssistant Professor of Computer Science, University of Southern California在 usc.edu 的电子邮件经过验证
Yushun DongUniversity of Virginia在 virginia.edu 的电子邮件经过验证

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Kaize Ding

Assistant Professor of Stats & Data Science, Northwestern University

在 northwestern.edu 的电子邮件经过验证 - 首页

Reliable Machine Learning Data-Efficient Learning Graph Neural Networks Anomaly Detection


标题按引用次数排序按年份排序按标题排序	引用次数引用次数	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	391	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	234	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	193	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	188	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	159	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	151	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	129	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	104	2021
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	78	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	74*	2022
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	71	2020
Inductive anomaly detection on attributed networks K Ding, J Li, N Agarwal, H Liu Proceedings of the Twenty-Ninth International Conference on International …, 2020	70	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	55	2021
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	53	2022
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	46	2021
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	38	2022
GOOD-D: On Unsupervised Graph Out-Of-Distribution Detection Y Liu, K Ding, H Liu, S Pan WSDM 2023, 2022	36	2022
Pygod: A python library for graph outlier detection K Liu, Y Dou, X Ding, X Hu, R Zhang, H Peng, L Sun, SY Philip Journal of Machine Learning Research 25 (141), 1-9, 2024	35	2024
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	33	2022
Cross-domain graph anomaly detection K Ding, K Shu, X Shan, J Li, H Liu IEEE Transactions on Neural Networks and Learning Systems 33 (6), 2406-2415, 2021	32	2021

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