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 WangTexas A&M, Google BrainVerified email at tamu.edu
Kai ShuAssistant Professor of Computer Science, Illinois Institute of TechnologyVerified email at iit.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
Zhen TanArizona State UniversityVerified email at asu.edu
Chenghao LiuSalesforce Research AsiaVerified email at salesforce.com
Song WangPhD. Student, University of VirginiaVerified email at virginia.edu
Zhe XuUniversity of Illinois Urbana-ChampaignVerified email at illinois.edu
Yingtong DouResearch Scientist, Visa Inc.Verified email at visa.com
Yue ZhaoAssistant Professor of Computer Science, University of Southern CaliforniaVerified email at usc.edu
Philip S. YuProfessor of Computer Science, University of Illinons at ChicagoVerified email at cs.uic.edu
Chuxu ZhangAssistant Professor, Computer Science, Brandeis UniversityVerified email at brandeis.edu
Yichuan LiWorcester Polytechnic InstituteVerified email at wpi.edu
Ziwei ZhuAssistant Professor at George Mason UniversityVerified email at gmu.edu
Nitesh V Chawla, Fellow ACM, IEEEFrank Freimann Professor of CSE.,Director, Lucy Family Institute for Data & Soc, Univ. of Notre DameVerified email at nd.edu
Yushun DongUniversity of VirginiaVerified email at virginia.edu
Nitin AgarwalJerry L. Maulden-Entergy Chair and Distinguished Professor of Information ScienceVerified email at ualr.edu

Kaize Ding

Assistant Professor of Stats & Data Science, Northwestern University

Verified email at asu.edu - Homepage

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


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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	281	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	163	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	124	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	120	2019
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	103	2020
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	99	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	89	2022
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	71	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	64	2020
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	53	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	45	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	35	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	33*	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	31	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	30	2022
Meta Propagation Networks for Graph Few-shot Semi-supervised Learning K Ding, J Wang, J Caverlee, H Liu AAAI 2022, 2022	26	2022
Fact-enhanced synthetic news generation K Shu, Y Li, K Ding, H Liu Proceedings of the AAAI Conference on Artificial Intelligence 35 (15), 13825 …, 2021	23	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	22	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	19	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	19	2021

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