Yi Wen

484 total citations · 1 hit paper
8 papers, 257 citations indexed

About

Yi Wen is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Urban Studies. According to data from OpenAlex, Yi Wen has authored 8 papers receiving a total of 257 indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Artificial Intelligence, 4 papers in Computer Vision and Pattern Recognition and 2 papers in Urban Studies. Recurrent topics in Yi Wen's work include Advanced Graph Neural Networks (4 papers), Advanced Clustering Algorithms Research (3 papers) and Face and Expression Recognition (3 papers). Yi Wen is often cited by papers focused on Advanced Graph Neural Networks (4 papers), Advanced Clustering Algorithms Research (3 papers) and Face and Expression Recognition (3 papers). Yi Wen collaborates with scholars based in China, Singapore and Hong Kong. Yi Wen's co-authors include Xinwang Liu, Siwei Wang, Ke Liang, En Zhu, Xihong Yang, Yue Liu, Sihang Zhou, Xinhang Wan, Xiangjun Dong and Wenxuan Tu and has published in prestigious journals such as IEEE Transactions on Neural Networks and Learning Systems, IEEE Transactions on Knowledge and Data Engineering and IEEE Transactions on Circuits and Systems for Video Technology.

In The Last Decade

Yi Wen

7 papers receiving 255 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.

One-Step Multi-View Clustering With Diverse Representation

2024 48 citations Xinhang Wan, Jiyuan Liu et al. IEEE Transactions on Neural Networks and Learning Systems profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Yi Wen China	6	187	162	24	22	18	8	257
Lazhar Labiod France	8	165 0.9×	84 0.5×	65 2.7×	25 1.1×	19 1.1×	25	218
Wang-Tao Zhou China	4	230 1.2×	296 1.8×	34 1.4×	16 0.7×	67 3.7×	6	426
Binyuan Hui China	9	214 1.1×	90 0.6×	21 0.9×	56 2.5×	5 0.3×	22	271
Weixuan Liang China	8	239 1.3×	266 1.6×	31 1.3×	14 0.6×	37 2.1×	17	356
Zhitong Zhao China	4	204 1.1×	289 1.8×	30 1.3×	11 0.5×	67 3.7×	8	371
Han Xiao China	7	159 0.9×	42 0.3×	10 0.4×	23 1.0×	11 0.6×	14	248
Shunxin Xiao China	7	154 0.8×	85 0.5×	41 1.7×	23 1.0×	6 0.3×	13	222
Pau Riba Spain	11	117 0.6×	239 1.5×	9 0.4×	19 0.9×	32 1.8×	23	293

Countries citing papers authored by Yi Wen

Since Specialization

Citations

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

Fields of papers citing papers by Yi Wen

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Yi Wen

This figure shows the co-authorship network connecting the top 25 collaborators of Yi Wen. A scholar is included among the top collaborators of Yi Wen 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 Yi Wen. Yi Wen 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:

8 of 8 papers shown

1.

Wang, Siwei, Xinwang Liu, Qing Liao, et al.. (2025). Scalable Multi-View Graph Clustering With Cross-View Corresponding Anchor Alignment. IEEE Transactions on Knowledge and Data Engineering. 37(5). 2932–2945. 5 indexed citations

2.

Wen, Yi, Yue Liu, Siwei Wang, et al.. (2025). Measure Domain's Gap: A Similar Domain Selection Principle for Multi-Domain Recommendation. 3156–3167.

3.

Wan, Xinhang, Jiyuan Liu, Xinwang Liu, et al.. (2024). One-Step Multi-View Clustering With Diverse Representation. IEEE Transactions on Neural Networks and Learning Systems. 36(3). 5774–5786. 48 indexed citations breakdown →

4.

Yang, Xihong, Yiqi Wang, Yue Liu, et al.. (2024). Mixed Graph Contrastive Network for Semi-supervised Node Classification. ACM Transactions on Knowledge Discovery from Data. 18(7). 1–19. 11 indexed citations

5.

Liang, Ke, Yue Liu, Sihang Zhou, et al.. (2023). Knowledge Graph Contrastive Learning Based on Relation-Symmetrical Structure. IEEE Transactions on Knowledge and Data Engineering. 36(1). 226–238. 69 indexed citations

6.

Yang, Xihong, Siwei Wang, Ke Liang, et al.. (2023). DealMVC: Dual Contrastive Calibration for Multi-view Clustering. 337–346. 60 indexed citations

7.

Wang, Siwei, Yi Wen, Ziming Wang, et al.. (2023). How to Construct Corresponding Anchors for Incomplete Multiview Clustering. IEEE Transactions on Circuits and Systems for Video Technology. 34(4). 2845–2860. 23 indexed citations

8.

Wen, Yi, Siwei Wang, Qing Liao, et al.. (2023). Unpaired Multi-View Graph Clustering With Cross-View Structure Matching. IEEE Transactions on Neural Networks and Learning Systems. 35(11). 16049–16063. 41 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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