Kaize Ding

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Huan LiuRegents Professor, Arizona State UniversityVerified email at asu.edu
Jundong LiAssistant Professor, University of VirginiaVerified email at virginia.edu
Jianling WangResearch Scientist, Google DeepMindVerified email at tamu.edu
Kai ShuAssistant Professor of Computer Science, Emory UniversityVerified email at emory.edu
James CaverleeProfessor, Computer Science and Engineering, Texas A&M UniversityVerified email at cse.tamu.edu
Hanghang TongUniversity of Illinois at Urbana-ChampaignVerified email at illinois.edu
Ruocheng GuoByteDance Research, LondonVerified email at bytedance.com
Shirui PanFull Professor, ARC Future Fellow, FQA, Director of TrustAGI Lab, Griffith UniversityVerified email at griffith.edu.au
Zhen TanPh.D. at Arizona State UniversityVerified email at asu.edu
Zhe XuUniversity of Illinois Urbana-ChampaignVerified email at illinois.edu
Yichuan LiWorcester Polytechnic InstituteVerified email at wpi.edu
Chuxu ZhangAssociate Professor of CSE, University of Connecticut (UConn)Verified email at uconn.edu
Song WangPh.D. Student, University of VirginiaVerified email at virginia.edu
Yingtong DouResearch Scientist, Visa Inc.Verified email at visa.com
Philip S. YuProfessor of Computer Science, University of Illinons at ChicagoVerified email at cs.uic.edu
Nitesh V Chawla, Fellow: ACM, IEEE,...Frank Freimann Professor of CSE.,Director, Lucy Family Institute for Data & Soc, Univ. of Notre DameVerified email at nd.edu
Ziwei ZhuAssistant Professor at George Mason UniversityVerified email at gmu.edu
Yue ZhaoAssistant Professor of Computer Science, University of Southern CaliforniaVerified email at usc.edu
Lu ChengAssistant Professor, UIC CSVerified email at uic.edu
Yushun DongAssistant Professor, Department of Computer Science, Florida State UniversityVerified email at fsu.edu

Kaize Ding

Assistant Professor of Stats & Data Science, Northwestern University

Verified email at northwestern.edu - Homepage

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


Title Sort by citations Sort by year Sort by title	Cited by Cited by	Year
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	453	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	255	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	225	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	209	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	181	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	164	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	139	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	116	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	96	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	93*	2022
Inductive anomaly detection on attributed networks K Ding, J Li, N Agarwal, H Liu Proceedings of the Twenty-Ninth International Conference on International …, 2020	82	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	73	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	62	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	60	2022
GOOD-D: On Unsupervised Graph Out-Of-Distribution Detection Y Liu, K Ding, H Liu, S Pan WSDM 2023, 2022	50	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	49	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	44	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	41	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	38	2022
Meta Propagation Networks for Graph Few-shot Semi-supervised Learning K Ding, J Wang, J Caverlee, H Liu AAAI 2022, 2022	37	2022

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