Peng Cui

21.1k citations

226 papers · 11.9k · 6 hit papers · h-index 51

Impact in

Statistical and Nonlinear Physics top 0.05%
- Complex Network Analysis Techniques
- Opinion Dynamics and Social Influence
Artificial Intelligence top 0.05%
- Advanced Graph Neural Networks
- Topic Modeling
- Domain Adaptation and Few-Shot Learning

Papers in

Artificial Intelligence 113
- Advanced Graph Neural Networks 57
- Domain Adaptation and Few-Shot Learning 20
Computer Vision and Pattern Recognition 75
- Advanced Image and Video Retrieval Techniques 22
- Video Analysis and Summarization 20
- Image Retrieval and Classification Techniques 20

Co-authors: Wenwu Zhu (73 shared papers)Daixin Wang (8 shared papers)Ziwei Zhang (15 shared papers)Jian Pei (17 shared papers)Shiqiang Yang (55 shared papers)Xiao Wang (6 shared papers)Meng Jiang (19 shared papers)Mingdong Ou (7 shared papers)
Journals: IEEE Transactions on Knowledge and Data Engineering (17 papers)IEEE Transactions on Multimedia (4 papers)Knowledge and Information Systems (3 papers)ACM Transactions on Knowledge Discovery from Data (3 papers)Data Mining and Knowledge Discovery (3 papers)
Partner nations: China United States Canada

In The Last Decade

Peng Cui

213 papers receiving 11.6k citations

Peng Cui's Hit Papers

6 papers align trajectories log scale

Stable learning establishes some common ground between causal inference and machine learning 2022 · 150 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 Nature Machine Intelligence
2020 IEEE Transactions on Knowledge and Data Engineering
2018 IEEE Transactions on Knowledge and Data Engineering
2017 Proceedings of the AAAI Conference on Artificial Intelligence
2016 Asymmetric Transitivity Preserving Graph Embedding
2016 Structural Deep Network Embedding

Peers

Countries citing papers authored by Peng Cui

Since Specialization

Citations

This map shows the geographic impact of Peng Cui'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 Peng Cui with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Peng Cui more than expected).

Fields of papers citing papers by Peng Cui

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Peng Cui. 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 Peng Cui. The network helps show where Peng Cui may publish in the future.

Co-authors

The 25 scholars most cited alongside Peng Cui, 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 Peng Cui Line = papers co-authored together Peng Cui 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 226 papers — load more, or switch the sort, to bring in the rest.

#	Work
1	Structural Deep Network Embedding Daixin Wang, Peng Cui, Wenwu Zhu Hit paper breakdown →	2016	1905
2	Deep Learning on Graphs: A Survey IEEE Transactions on Knowledge and Data Engineering ·Ziwei Zhang, Peng Cui, Wenwu Zhu Hit paper breakdown →	2020	1040
3	A Survey on Network Embedding IEEE Transactions on Knowledge and Data Engineering ·Peng Cui, Xiao Wang, Jian Pei, Wenwu Zhu Hit paper breakdown →	2018	878
4	Asymmetric Transitivity Preserving Graph Embedding Mingdong Ou, Peng Cui, Jian Pei, Ziwei Zhang, Wenwu Zhu Hit paper breakdown →	2016	818
5	Community Preserving Network Embedding Proceedings of the AAAI Conference on Artificial Intelligence ·Xiao Wang, Peng Cui, Jing Wang, Jian Pei, Wenwu Zhu, Shiqiang Yang Hit paper breakdown →	2017	643
6	Robust Graph Convolutional Networks Against Adversarial Attacks Dingyuan Zhu, Ziwei Zhang, Peng Cui, Wenwu Zhu	2019	257
7	Social contextual recommendation Rare & Special e-Zone (The Hong Kong University of Science and Technology) ·Meng Jiang, Peng Cui, Rui Liu, Qiang Yang, Fei Wang, Wenwu Zhu, Shiqiang Yang	2012	212
8	Deep Stable Learning for Out-Of-Distribution Generalization Xingxuan Zhang, Peng Cui, Renzhe Xu, Linjun Zhou, Yue He, Zheyan Shen	2021	192
9	Disentangled Self-Supervision in Sequential Recommenders Jianxin Ma, Chang Zhou, Hongxia Yang, Peng Cui, Xin Wang, Wenwu Zhu	2020	160
10	Stable learning establishes some common ground between causal inference and machine learning Nature Machine Intelligence ·Peng Cui, Susan Athey Hit paper breakdown →	2022	150
11	Scalable Recommendation with Social Contextual Information IEEE Transactions on Knowledge and Data Engineering ·Meng Jiang, Peng Cui, Fei Wang, Wenwu Zhu, Shiqiang Yang	2014	149
12	Deep Recursive Network Embedding with Regular Equivalence Ke Tu, Peng Cui, Xiao Wang, Philip S. Yu, Wenwu Zhu	2018	136
13	Arbitrary-Order Proximity Preserved Network Embedding Ziwei Zhang, Peng Cui, Xiao Wang, Jian Pei, Xuanrong Yao, Wenwu Zhu	2018	135
14	Structural Deep Embedding for Hyper-Networks Proceedings of the AAAI Conference on Artificial Intelligence ·Ke Tu, Peng Cui, Xiao Wang, Fei Wang, Wenwu Zhu	2018	122
15	CatchSync Meng Jiang, Peng Cui, Alex Beutel, Christos Faloutsos, Shiqiang Yang	2014	121
16	Network embedding in biomedical data science Briefings in Bioinformatics ·Chang Su, Jie Tong, Yongjun Zhu, Peng Cui, Fei Wang	2018	120
17	High-order Proximity Preserved Embedding For Dynamic Networks IEEE Transactions on Knowledge and Data Engineering ·Dingyuan Zhu, Peng Cui, Ziwei Zhang, Jian Pei, Wenwu Zhu	2018	115
18	Graph Neural Networks: Foundation, Frontiers and Applications Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining ·Lingfei Wu, Peng Cui, Jian Pei, Liang Zhao, Xiaojie Guo	2022	112
19	Cascading outbreak prediction in networks Peng Cui, Shifei Jin, Linyun Yu, Fei Wang, Wenwu Zhu, Shiqiang Yang	2013	110
20	Towards Non-I.I.D. image classification: A dataset and baselines Pattern Recognition ·Yue He, Zheyan Shen, Peng Cui	2020	107

About Peng Cui

Peng Cui is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Statistical and Nonlinear Physics, Information Systems and Computer Networks and Communications, having authored 226 papers that have together received 11.9k indexed citations. Recurring topics across this work include Complex Network Analysis Techniques (58 papers), Advanced Graph Neural Networks (57 papers), Recommender Systems and Techniques (38 papers), Advanced Image and Video Retrieval Techniques (22 papers), Domain Adaptation and Few-Shot Learning (20 papers), Video Analysis and Summarization (20 papers), Image Retrieval and Classification Techniques (20 papers) and Opinion Dynamics and Social Influence (19 papers). The work is most often cited by research in Statistical and Nonlinear Physics (4.2k citations), Artificial Intelligence (8.0k citations), Computational Mathematics (87 citations), Information Systems (2.9k citations) and Computer Vision and Pattern Recognition (2.5k citations). Peng Cui has collaborated with scholars based in China, United States and Canada. Frequent co-authors include Wenwu Zhu, Daixin Wang, Ziwei Zhang, Jian Pei, Shiqiang Yang, Xiao Wang, Meng Jiang, Mingdong Ou, Christos Faloutsos and Zheyan Shen. Their work appears in journals such as IEEE Transactions on Knowledge and Data Engineering, IEEE Transactions on Multimedia, Knowledge and Information Systems, ACM Transactions on Knowledge Discovery from Data and Data Mining and Knowledge Discovery.

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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