Chuan Zhou

5.4k total citations · 2 hit papers
154 papers, 3.3k citations indexed

About

Chuan Zhou is a scholar working on Artificial Intelligence, Information Systems and Statistical and Nonlinear Physics. According to data from OpenAlex, Chuan Zhou has authored 154 papers receiving a total of 3.3k indexed citations (citations by other indexed papers that have themselves been cited), including 91 papers in Artificial Intelligence, 44 papers in Information Systems and 44 papers in Statistical and Nonlinear Physics. Recurrent topics in Chuan Zhou's work include Advanced Graph Neural Networks (59 papers), Complex Network Analysis Techniques (44 papers) and Recommender Systems and Techniques (25 papers). Chuan Zhou is often cited by papers focused on Advanced Graph Neural Networks (59 papers), Complex Network Analysis Techniques (44 papers) and Recommender Systems and Techniques (25 papers). Chuan Zhou collaborates with scholars based in China, Australia and United States. Chuan Zhou's co-authors include Shirui Pan, Jia Wu, Li Guo, Yixin Liu, Zhao Li, Peng Zhang, Xingquan Zhu, Hong Yang, Chen Gong and Peng Zhang and has published in prestigious journals such as PLoS ONE, IEEE Transactions on Pattern Analysis and Machine Intelligence and Journal of Power Sources.

In The Last Decade

Chuan Zhou

146 papers receiving 3.3k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Graph Self-Supervised Learning: A Survey

2022 330 citations Shirui Pan, Chuan Zhou et al. IEEE Transactions on Knowledge and Data Engineering profile →
Anomaly Detection on Attributed Networks via Contrastive Self-Supervised Learning

2021 240 citations Zhao Li, Shirui Pan et al. IEEE Transactions on Neural Networks and Learning Systems profile →

Peers

Chuan Zhou

AI

SNP

IS

CNC

CVPR

Zhoujun Li China

Tinghuai Ma China

Jie Yin Australia

Xiangfeng Luo China

Bryan Hooi Singapore

Sang‐Wook Kim South Korea

Hao Peng China

Ziyu Guan China

Fei Hao China

Zheng Xu China

Zhoujun Li China

Chuan Zhou

3.5k ×1.7

AI

548 ×0.6

SNP

1.3k ×1.5

IS

644 ×1.2

CNC

1.1k ×2.5

CVPR

Citations per year, relative to Chuan Zhou Chuan Zhou (= 1×) peers Zhoujun Li

Countries citing papers authored by Chuan Zhou

Since Specialization

Citations

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

Fields of papers citing papers by Chuan Zhou

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Chuan Zhou

This figure shows the co-authorship network connecting the top 25 collaborators of Chuan Zhou. A scholar is included among the top collaborators of Chuan Zhou based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Chuan Zhou. Chuan Zhou is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

1.

Lin, Xixun, Yanan Cao, Sun Nan, et al.. (2025). Conformal Graph-level Out-of-distribution Detection with Adaptive Data Augmentation. 4755–4765. 3 indexed citations

2.

Sheng, Jiawei, et al.. (2025). Graph Wave Networks. 1365–1379.

3.

Chen, Weifeng, Peng Chen, Wanqing Chen, et al.. (2025). Significantly improving the performance of SrCo0·8Nb0·1Ta0·1O3-δ perovskite-based oxygen electrode on YSZ/Ni-supported YSZ|GDC solid oxide cells. International Journal of Hydrogen Energy. 129. 20–27. 1 indexed citations

4.

Wu, Jia, Jian Yang, Quan Z. Sheng, et al.. (2024). State of the Art and Potentialities of Graph-level Learning. ACM Computing Surveys. 57(2). 1–40. 13 indexed citations

5.

Huang, Changqin, Yunliang Jiang, Ming Li, et al.. (2024). Flow2GNN: Flexible Two-Way Flow Message Passing for Enhancing GNNs Beyond Homophily. IEEE Transactions on Cybernetics. 54(11). 6607–6618. 15 indexed citations

6.

Zhou, Chuan, et al.. (2023). A hierarchical federated learning model with adaptive model parameter aggregation. Computer Science and Information Systems. 20(3). 1037–1060. 2 indexed citations

7.

Zhou, Chuan, et al.. (2023). Trusted edge and cross-domain privacy enhancement model under multi-blockchain. Computer Networks. 234. 109881–109881. 5 indexed citations

8.

Zhang, Miao, et al.. (2023). Auto-HeG: Automated Graph Neural Network on Heterophilic Graphs. Griffith Research Online (Griffith University, Queensland, Australia). 611–620. 17 indexed citations

9.

Zhang, Peng, et al.. (2023). HGNAS++: Efficient Architecture Search for Heterogeneous Graph Neural Networks. IEEE Transactions on Knowledge and Data Engineering. 35(9). 9448–9461. 22 indexed citations

10.

Zhou, Chuan, et al.. (2023). Dynamic Graph Representation Learning via Coupling-Process Model. IEEE Transactions on Neural Networks and Learning Systems. 35(9). 12383–12395. 5 indexed citations

11.

Lin, Xixun, Chuan Zhou, Jia Wu, et al.. (2023). Towards Flexible and Adaptive Neural Process for Cold-Start Recommendation. IEEE Transactions on Knowledge and Data Engineering. 36(4). 1815–1828. 8 indexed citations

12.

Li, Zhao, Jiaming Huang, Jia Wu, et al.. (2022). eFraudCom: An E-commerce Fraud Detection System via Competitive Graph Neural Networks. ACM Transactions on Information Systems. 40(3). 1–29. 90 indexed citations

13.

Lin, Xixun, Jiangxia Cao, Peng Zhang, et al.. (2021). Disentangled Deep Multivariate Hawkes Process for Learning Event Sequences. 360–369. 6 indexed citations

14.

Zhang, Miao, Huiqi Li, Shirui Pan, et al.. (2020). One-Shot Neural Architecture Search: Maximising Diversity to Overcome Catastrophic Forgetting. IEEE Transactions on Pattern Analysis and Machine Intelligence. 43(9). 2921–2935. 49 indexed citations

15.

Wu, Jia, Shirui Pan, Chuan Zhou, et al.. (2018). Advances in Processing, Mining, and Learning Complex Data: From Foundations to Real‐World Applications. Complexity. 2018(1). 72 indexed citations

16.

Jiang, Wei, Chuan Zhou, Daobin Ji, et al.. (2017). [Comparison of Relationship Between Conduction and Algal Bloom in Pengxi River and Modao River in Three Gorges Reservoir].. PubMed. 38(6). 2326–2335. 6 indexed citations

17.

Zhou, Chuan, Jianjun Yu, Li Fu, et al.. (2016). [Temporal and Spatial Distribution of Environmental Factors and Phytoplankton During Algal Bloom Season in Pengxi River, Three Gorges Reservoir].. PubMed. 37(3). 873–83. 5 indexed citations

18.

Zhang, Lei, et al.. (2015). [Temporal and Spatial Variation of Nutrients and Chlorophyll a, and Their Relationship in Pengxi River Backwater Area, Three Gorges Reservoir].. PubMed. 36(6). 2061–9. 9 indexed citations

19.

Zhou, Chuan & Zheng Niu. (2009). Operational retrieving of land surface evapotranspiration based on remote sensing technology.. Nongye gongcheng xuebao. 25(7). 124–130. 2 indexed citations

20.

Shi, Jiazheng, Berkman Sahiner, Heang‐Ping Chan, et al.. (2008). Characterization of Mammographic Masses Based on Level Set Segmentation with New Image Features and Patient Information. PubMed Central. 1 indexed citations

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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