Kaize Ding

3.0k citations

63 papers · 1.7k · 2 hit papers · h-index 19

Impact in

Artificial Intelligence top 1%
- Advanced Graph Neural Networks
- Anomaly Detection Techniques and Applications
- Topic Modeling
- Domain Adaptation and Few-Shot Learning
Statistical and Nonlinear Physics top 2%
- Complex Network Analysis Techniques

Papers in

Artificial Intelligence 50
- Advanced Graph Neural Networks 35
- Topic Modeling 18
- Domain Adaptation and Few-Shot Learning 15
- Anomaly Detection Techniques and Applications 11
- Text and Document Classification Technologies 6
Information Systems 15
- Recommender Systems and Techniques 9

Co-authors: Huan Liu (34 shared papers)Jundong Li (15 shared papers)Jianling Wang (8 shared papers)James Caverlee (5 shared papers)Hanghang Tong (6 shared papers)Liangjie Hong (1 shared paper)Zhe Xu (3 shared papers)Dingcheng Li (2 shared papers)
Journals: IEEE Transactions on Neural Networks and Learning Systems (2 papers)Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery (1 paper)ACM Transactions on Knowledge Discovery from Data (1 paper)Knowledge and Information Systems (1 paper)Information Fusion (1 paper)
Partner nations: United States China Australia

In The Last Decade

Kaize Ding

57 papers receiving 1.7k citations

Kaize Ding's Hit Papers

align trajectories log scale

Data Augmentation for Deep Graph Learning 2022 · 145 citations

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if any of the following hold:

it has ≥500 total citations;
it reaches ≥1.5× the top-1% citation threshold for papers in the same subfield and year (the threshold is the minimum needed to enter the top 1%, not the average within it);
it reaches the top citation threshold in at least one of its specific research topics.

2022 ACM SIGKDD Explorations Newsletter
2019 Society for Industrial and Applied Mathematics eBooks

Peers

Countries citing papers authored by Kaize Ding

Since Specialization

Citations

This map shows the geographic impact of Kaize Ding's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Kaize Ding with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Kaize Ding more than expected).

Fields of papers citing papers by Kaize Ding

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Kaize Ding. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Kaize Ding. The network helps show where Kaize Ding may publish in the future.

Co-authors

The 25 scholars most cited alongside Kaize Ding, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Kaize Ding Line = papers co-authored together Kaize Ding links everyone, so they are left out of the graph.

All Works

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20 of 20 papers shown

Showing the 20 most-cited of 63 papers — load more, or switch the sort, to bring in the rest.

#	Work
1	Deep Anomaly Detection on Attributed Networks Society for Industrial and Applied Mathematics eBooks ·Kaize Ding, Jundong Li, Rohit Bhanushali, Huan Liu Hit paper breakdown →	2019	296
2	Next-item Recommendation with Sequential Hypergraphs Jianling Wang, Kaize Ding, Liangjie Hong, Huan Liu, James Caverlee	2020	189
3	Be More with Less: Hypergraph Attention Networks for Inductive Text Classification Kaize Ding, Jianling Wang, Jundong Li, Dingcheng Li, Huan Liu	2020	152
4	Data Augmentation for Deep Graph Learning ACM SIGKDD Explorations Newsletter ·Kaize Ding, Zhe Xu, Hanghang Tong, Huan Liu Hit paper breakdown →	2022	145
5	Combating disinformation in a social media age Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery ·Kai Shu, Amrita Bhattacharjee, Faisal Alatawi, Tahora H. Nazer, Kaize Ding, Mansooreh Karami, Huan Liu	2020	111
6	Interactive Anomaly Detection on Attributed Networks Kaize Ding, Jundong Li, Huan Liu	2019	104
7	Few-shot Network Anomaly Detection via Cross-network Meta-learning Kaize Ding, Qinghai Zhou, Hanghang Tong, Huan Liu	2021	81
8	Session-based Recommendation with Hypergraph Attention Networks Society for Industrial and Applied Mathematics eBooks ·Jianling Wang, Kaize Ding, Ziwei Zhu, James Caverlee	2021	72
9	Graph Few-shot Learning with Attribute Matching Ning Wang, Minnan Luo, Kaize Ding, Lingling Zhang, Jundong Li, Qinghua Zheng	2020	54
10	Inductive Anomaly Detection on Attributed Networks Kaize Ding, Jundong Li, Nitin Agarwal, Huan Liu	2020	52
11	Meta Propagation Networks for Graph Few-shot Semi-supervised Learning Proceedings of the AAAI Conference on Artificial Intelligence ·Kaize Ding, Jianling Wang, James Caverlee, Huan Liu	2022	38
12	Cross-Domain Graph Anomaly Detection IEEE Transactions on Neural Networks and Learning Systems ·Kaize Ding, Kai Shu, Xuan Shan, Jundong Li, Huan Liu	2021	34
13	Sequential Recommendation for Cold-start Users with Meta Transitional Learning Jianling Wang, Kaize Ding, James Caverlee	2021	34
14	Graph Few-shot Class-incremental Learning Zhen Tan, Kaize Ding, Ruocheng Guo, Huan Liu	2022	32
15	Learning Strong Graph Neural Networks with Weak Information arXiv (Cornell University) ·Yixin Liu, Kaize Ding, Jianling Wang, Vincent C. S. Lee, Huan Liu, Shirui Pan	2023	28
16	Task-Adaptive Few-shot Node Classification Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining ·Song Wang, Kaize Ding, Chuxu Zhang, Chen Chen, Jundong Li	2022	23
17	Few-Shot Learning on Graphs Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence ·Chuxu Zhang, Kaize Ding, Jundong Li, Xiangliang Zhang, Yanfang Ye, Nitesh V. Chawla, Huan Liu	2022	20
18	Federated Few-shot Learning Song Wang, Xingbo Fu, Kaize Ding, Chen Chen, Huiyuan Chen, Jundong Li	2023	19
19	Key Opinion Leaders in Recommendation Systems Jianling Wang, Kaize Ding, Ziwei Zhu, Yin Zhang⋆, James Caverlee	2020	18
20	Eliciting Structural and Semantic Global Knowledge in Unsupervised Graph Contrastive Learning Proceedings of the AAAI Conference on Artificial Intelligence ·Kaize Ding, Yan-Cheng Wang, (unknown), Huan Liu	2023	17

About Kaize Ding

Kaize Ding is a scholar working on Artificial Intelligence, Information Systems, Management Science and Operations Research, Computer Vision and Pattern Recognition and Statistical and Nonlinear Physics, having authored 63 papers that have together received 1.7k indexed citations. Recurring topics across this work include Advanced Graph Neural Networks (35 papers), Topic Modeling (18 papers), Domain Adaptation and Few-Shot Learning (15 papers), Anomaly Detection Techniques and Applications (11 papers), Recommender Systems and Techniques (9 papers), Complex Network Analysis Techniques (6 papers), Text and Document Classification Technologies (6 papers) and Data Quality and Management (5 papers). The work is most often cited by research in Artificial Intelligence (1.4k citations), Statistical and Nonlinear Physics (325 citations), Information Systems (469 citations), Computer Networks and Communications (406 citations) and Management Science and Operations Research (109 citations). Kaize Ding has collaborated with scholars based in United States, China and Australia. Frequent co-authors include Huan Liu, Jundong Li, Jianling Wang, James Caverlee, Hanghang Tong, Liangjie Hong, Zhe Xu, Dingcheng Li, Kai Shu and Ziwei Zhu. Their work appears in journals such as IEEE Transactions on Neural Networks and Learning Systems, Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery, ACM Transactions on Knowledge Discovery from Data, Knowledge and Information Systems and Information Fusion.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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