Wenxuan Tu

2.8k total citations · 6 hit papers
50 papers, 1.6k citations indexed

About

Wenxuan Tu is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Statistical and Nonlinear Physics. According to data from OpenAlex, Wenxuan Tu has authored 50 papers receiving a total of 1.6k indexed citations (citations by other indexed papers that have themselves been cited), including 37 papers in Artificial Intelligence, 20 papers in Computer Vision and Pattern Recognition and 11 papers in Statistical and Nonlinear Physics. Recurrent topics in Wenxuan Tu's work include Advanced Graph Neural Networks (23 papers), Complex Network Analysis Techniques (11 papers) and Recommender Systems and Techniques (8 papers). Wenxuan Tu is often cited by papers focused on Advanced Graph Neural Networks (23 papers), Complex Network Analysis Techniques (11 papers) and Recommender Systems and Techniques (8 papers). Wenxuan Tu collaborates with scholars based in China, United States and Hong Kong. Wenxuan Tu's co-authors include Xinwang Liu, Sihang Zhou, En Zhu, Yue Liu, Siwei Wang, Ke Liang, Xihong Yang, Jieren Cheng, Zhiping Cai and Meng Liu and has published in prestigious journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Transactions on Geoscience and Remote Sensing and IEEE Transactions on Image Processing.

In The Last Decade

Wenxuan Tu

45 papers receiving 1.6k citations

Hit Papers

Scalable Multi-view Subspace Clustering with Unified Anchors 2021 2026 2022 2024 2021 2022 2023 2024 2024 50 100 150
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.

  1. Scalable Multi-view Subspace Clustering with Unified Anchors
    2021 172 citations Siwei Wang, Sihang Zhou et al. profile →
  2. Deep Graph Clustering via Dual Correlation Reduction
    2022 141 citations Yue Liu, Wenxuan Tu et al. Proceedings of the AAAI Conference on Artificial Intelligence profile →
  3. Simple Contrastive Graph Clustering
    2023 99 citations Yue Liu, Xihong Yang et al. IEEE Transactions on Neural Networks and Learning Systems profile →
  4. A Survey of Knowledge Graph Reasoning on Graph Types: Static, Dynamic, and Multi-Modal
    2024 76 citations Ke Liang, Lingyuan Meng et al. IEEE Transactions on Pattern Analysis and Machine Intelligence profile →
  5. Contrastive Multiview Subspace Clustering of Hyperspectral Images Based on Graph Convolutional Networks
    2024 73 citations Renxiang Guan, Wenxuan Tu et al. IEEE Transactions on Geoscience and Remote Sensing profile →
  6. Self-Supervised Temporal Graph Learning With Temporal and Structural Intensity Alignment
    2024 58 citations Meng Liu, Ke Liang 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 Career Trend Papers Cites
Wenxuan Tu China 21 1.2k 794 319 216 151 50 1.6k
Deli Zhao China 18 798 0.7× 708 0.9× 371 1.2× 126 0.6× 90 0.6× 57 1.5k
Yan Pan China 15 585 0.5× 1.4k 1.7× 128 0.4× 98 0.5× 186 1.2× 70 1.8k
Yuheng Jia China 21 533 0.5× 749 0.9× 151 0.5× 78 0.4× 204 1.4× 72 1.2k
Wei Xia China 20 716 0.6× 1.1k 1.4× 137 0.4× 155 0.7× 186 1.2× 58 1.7k
Liang Du China 23 801 0.7× 917 1.2× 106 0.3× 67 0.3× 200 1.3× 76 1.4k
Mingjing Du China 14 729 0.6× 435 0.5× 291 0.9× 75 0.3× 70 0.5× 26 962
Handong Zhao United States 20 1.0k 0.9× 1.3k 1.6× 47 0.1× 130 0.6× 169 1.1× 76 1.7k
A. Topchy United States 14 1.3k 1.1× 655 0.8× 349 1.1× 131 0.6× 108 0.7× 17 1.6k
Xijiong Xie China 19 825 0.7× 1.1k 1.3× 32 0.1× 63 0.3× 250 1.7× 50 1.6k
Yunhao Yuan China 21 582 0.5× 868 1.1× 46 0.1× 123 0.6× 275 1.8× 103 1.5k

Countries citing papers authored by Wenxuan Tu

Since Specialization
Citations

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

Fields of papers citing papers by Wenxuan Tu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Wenxuan Tu

This figure shows the co-authorship network connecting the top 25 collaborators of Wenxuan Tu. A scholar is included among the top collaborators of Wenxuan Tu 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 Wenxuan Tu. Wenxuan Tu is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Tu, Wenxuan, Sihang Zhou, Xinwang Liu, et al.. (2025). WAGE: Weight-Sharing Attribute-Missing Graph Autoencoder. IEEE Transactions on Pattern Analysis and Machine Intelligence. 47(7). 5760–5777. 1 indexed citations
2.
Guan, Renxiang, Siwei Wang, Wenxuan Tu, et al.. (2025). Multi-view Graph Clustering with Dual Relation Optimization for Remote Sensing Data. 7346–7355.
3.
Guan, Renxiang, Wenxuan Tu, Dayu Hu, et al.. (2025). Prototype-Driven Multi-View Attribute-Missing Graph Clustering. IEEE Transactions on Multimedia. 27. 9454–9466.
4.
Liang, Ke, Lingyuan Meng, Meng Liu, et al.. (2024). A Survey of Knowledge Graph Reasoning on Graph Types: Static, Dynamic, and Multi-Modal. IEEE Transactions on Pattern Analysis and Machine Intelligence. 46(12). 9456–9478. 76 indexed citations breakdown →
5.
Wang, Siwei, Xinwang Liu, Suyuan Liu, Wenxuan Tu, & En Zhu. (2024). Scalable and Structural Multi-View Graph Clustering With Adaptive Anchor Fusion. IEEE Transactions on Image Processing. 33. 4627–4639. 23 indexed citations
6.
Yu, Hao, Ke Liang, Dayu Hu, et al.. (2024). GZOO: Black-Box Node Injection Attack on Graph Neural Networks via Zeroth-Order Optimization. IEEE Transactions on Knowledge and Data Engineering. 37(1). 319–333. 5 indexed citations
7.
Liang, Ke, Sihang Zhou, Meng Liu, et al.. (2024). Hawkes-Enhanced Spatial-Temporal Hypergraph Contrastive Learning Based on Criminal Correlations. Proceedings of the AAAI Conference on Artificial Intelligence. 38(8). 8733–8741. 5 indexed citations
8.
Wang, Siwei, Zhibin Dong, Wenxuan Tu, et al.. (2024). A Non-parametric Graph Clustering Framework for Multi-View Data. Proceedings of the AAAI Conference on Artificial Intelligence. 38(15). 16558–16567. 18 indexed citations
9.
Tu, Wenxuan, Renxiang Guan, Sihang Zhou, et al.. (2024). Attribute-Missing Graph Clustering Network. Proceedings of the AAAI Conference on Artificial Intelligence. 38(14). 15392–15401. 25 indexed citations
10.
Tu, Wenxuan, Bin Xiao, Xinwang Liu, et al.. (2024). Revisiting Initializing Then Refining: An Incomplete and Missing Graph Imputation Network. IEEE Transactions on Neural Networks and Learning Systems. 36(2). 3244–3257. 11 indexed citations
11.
Liang, Ke, Lingyuan Meng, Yue Liu, et al.. (2024). Simple Yet Effective: Structure Guided Pre-trained Transformer for Multi-modal Knowledge Graph Reasoning. 1554–1563. 4 indexed citations
12.
Guan, Renxiang, Wenxuan Tu, Jun Wang, et al.. (2024). Contrastive Multiview Subspace Clustering of Hyperspectral Images Based on Graph Convolutional Networks. IEEE Transactions on Geoscience and Remote Sensing. 62. 1–14. 73 indexed citations breakdown →
13.
Tu, Wenxuan, Qing Liao, Sihang Zhou, et al.. (2023). RARE: Robust Masked Graph Autoencoder. IEEE Transactions on Knowledge and Data Engineering. 36(10). 5340–5353. 19 indexed citations
14.
Liu, Yue, Xihong Yang, Sihang Zhou, et al.. (2023). Hard Sample Aware Network for Contrastive Deep Graph Clustering. Proceedings of the AAAI Conference on Artificial Intelligence. 37(7). 8914–8922. 64 indexed citations
15.
Liu, Yue, Xihong Yang, Sihang Zhou, et al.. (2023). Simple Contrastive Graph Clustering. IEEE Transactions on Neural Networks and Learning Systems. 35(10). 13789–13800. 99 indexed citations breakdown →
16.
Tu, Wenxuan, et al.. (2023). Hierarchically Contrastive Hard Sample Mining for Graph Self-Supervised Pretraining. IEEE Transactions on Neural Networks and Learning Systems. 35(11). 16748–16761. 18 indexed citations
17.
Yang, Xihong, Yue Liu, Sihang Zhou, et al.. (2023). Cluster-Guided Contrastive Graph Clustering Network. Proceedings of the AAAI Conference on Artificial Intelligence. 37(9). 10834–10842. 68 indexed citations
18.
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
19.
Liang, Ke, Lingyuan Meng, Meng Liu, et al.. (2023). Learn from Relational Correlations and Periodic Events for Temporal Knowledge Graph Reasoning. 1559–1568. 50 indexed citations
20.
Liu, Xinwang, Li Liu, Qing Liao, et al.. (2021). One Pass Late Fusion Multi-view Clustering. International Conference on Machine Learning. 6850–6859. 11 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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