World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
39
Citations
8370
World Ranking
9609
National Ranking
1214

Shu Wu publication distribution in Computer Science in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Computer Science in 2026. The highlighted bar marks where Shu Wu sits on this spectrum.

32–41 publications: 7 scientists 42–51 publications: 22 scientists 52–61 publications: 82 scientists 62–71 publications: 134 scientists 72–81 publications: 249 scientists 82–91 publications: 324 scientists 92–101 publications: 421 scientists 102–111 publications: 420 scientists 112–121 publications: 497 scientists 122–131 publications: 544 scientists 132–141 publications: 555 scientists 142–151 publications: 609 scientists 152–161 publications: 559 scientists 162–171 publications: 534 scientists 172–181 publications: 556 scientists 182–191 publications: 583 scientists 192–201 publications: 519 scientists 202–211 publications: 508 scientists 212–221 publications: 490 scientists 222–231 publications: 437 scientists 232–241 publications: 423 scientists 242–251 publications: 408 scientists 252–261 publications: 377 scientists 262–271 publications: 301 scientists 272–281 publications: 335 scientists 282–291 publications: 320 scientists 292–301 publications: 293 scientists 302–311 publications: 250 scientists 312–321 publications: 238 scientists 322–331 publications: 206 scientists 332–341 publications: 209 scientists 342–351 publications: 208 scientists 352–361 publications: 162 scientists 362–371 publications: 176 scientists 372–381 publications: 127 scientists 382–391 publications: 158 scientists 392–401 publications: 128 scientists 402–411 publications: 104 scientists 412–421 publications: 94 scientists 422–431 publications: 99 scientists 432–441 publications: 83 scientists 442–451 publications: 108 scientists 452–461 publications: 73 scientists 462–471 publications: 77 scientists 472–481 publications: 69 scientists 482–491 publications: 84 scientists 492–501 publications: 62 scientists 502–511 publications: 54 scientists 512–521 publications: 57 scientists 522–531 publications: 51 scientists 532–541 publications: 51 scientists 542–551 publications: 32 scientists 552–561 publications: 38 scientists 562–571 publications: 28 scientists 572–581 publications: 43 scientists 582–591 publications: 33 scientists 592–601 publications: 41 scientists 602–611 publications: 32 scientists 612–621 publications: 28 scientists 622–631 publications: 25 scientists 632–641 publications: 27 scientists 642–651 publications: 17 scientists 652–661 publications: 20 scientists 662–671 publications: 17 scientists 672–681 publications: 15 scientists 682–691 publications: 14 scientists 692–701 publications: 21 scientists 702–711 publications: 13 scientists 712–721 publications: 12 scientists 722–731 publications: 19 scientists 732–741 publications: 14 scientists 742–751 publications: 12 scientists 752–761 publications: 10 scientists 762–771 publications: 10 scientists 772–781 publications: 11 scientists 782–791 publications: 10 scientists 792–801 publications: 11 scientists 802–811 publications: 8 scientists 812–821 publications: 8 scientists 822–831 publications: 7 scientists 832–841 publications: 11 scientists 842–851 publications: 10 scientists 852–861 publications: 5 scientists 862–871 publications: 9 scientists 872–881 publications: 4 scientists 882–891 publications: 6 scientists 892–901 publications: 3 scientists 902–911 publications: 6 scientists 912–921 publications: 3 scientists 922–931 publications: 2 scientists 932–941 publications: 2 scientists 942–951 publications: 2 scientists 952–961 publications: 3 scientists 962–971 publications: 3 scientists 972–981 publications: 3 scientists 982–990 publications: 5 scientists 991+ publications: 100 scientists
32 publications 991+

This scientist: 188 publications — 42nd percentile

42% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 991 publications or more.

Shu Wu D-index placement in Computer Science in 2026

The chart shows the D-index (discipline H-index) distribution of Computer Science scientists ranked by Research.com in 2026. The highlighted bar marks where Shu Wu sits on this spectrum.

30–31 D-Index: 879 scientists 32–33 D-Index: 983 scientists 34–35 D-Index: 918 scientists 36–37 D-Index: 990 scientists 38–39 D-Index: 968 scientists 40–41 D-Index: 907 scientists 42–43 D-Index: 821 scientists 44–45 D-Index: 763 scientists 46–47 D-Index: 689 scientists 48–49 D-Index: 543 scientists 50–51 D-Index: 543 scientists 52–53 D-Index: 518 scientists 54–55 D-Index: 500 scientists 56–57 D-Index: 458 scientists 58–59 D-Index: 400 scientists 60–61 D-Index: 337 scientists 62–63 D-Index: 308 scientists 64–65 D-Index: 292 scientists 66–67 D-Index: 249 scientists 68–69 D-Index: 213 scientists 70–71 D-Index: 192 scientists 72–73 D-Index: 189 scientists 74–75 D-Index: 165 scientists 76–77 D-Index: 139 scientists 78–79 D-Index: 119 scientists 80–81 D-Index: 121 scientists 82–83 D-Index: 113 scientists 84–85 D-Index: 88 scientists 86–87 D-Index: 87 scientists 88–89 D-Index: 75 scientists 90–91 D-Index: 69 scientists 92–93 D-Index: 57 scientists 94–95 D-Index: 46 scientists 96–97 D-Index: 38 scientists 98–99 D-Index: 34 scientists 100–101 D-Index: 36 scientists 102–103 D-Index: 27 scientists 104–105 D-Index: 37 scientists 106–107 D-Index: 18 scientists 108–109 D-Index: 31 scientists 110–111 D-Index: 19 scientists 112–113 D-Index: 16 scientists 114–115 D-Index: 12 scientists 116–117 D-Index: 20 scientists 118–119 D-Index: 15 scientists 120–121 D-Index: 5 scientists 122–123 D-Index: 20 scientists 124–125 D-Index: 8 scientists 126–127 D-Index: 5 scientists 128–129 D-Index: 7 scientists 130 D-Index: 3 scientists 131+ D-Index: 98 scientists
30 D-Index 131+

This scientist: 39 D-Index — 33rd percentile

33% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 131 D-Index or more.

Overview

Shu Wu is affiliated with the Chinese Academy of Sciences in China and has an extensive publication record primarily in the field of computer science, with a focus on artificial intelligence and related disciplines.

Their research spans multiple subfields, including artificial intelligence, information systems, computer vision and pattern recognition, sociology and political science, and statistical and nonlinear physics.

Shu Wu's contributions cover a variety of main topics such as:

  • Advanced Graph Neural Networks
  • Topic Modeling
  • Recommender Systems and Techniques
  • Misinformation and Its Impacts
  • Domain Adaptation and Few-Shot Learning
  • Complex Network Analysis Techniques
  • Natural Language Processing Techniques

Notable recent papers by Shu Wu include:

  • Deep Graph Contrastive Representation Learning, 2020, arXiv (Cornell University)
  • Dynamic Graph Neural Networks for Sequential Recommendation, 2022, IEEE Transactions on Knowledge and Data Engineering
  • Latent Structure Mining With Contrastive Modality Fusion for Multimedia Recommendation, 2022, IEEE Transactions on Knowledge and Data Engineering
  • Evidence-aware Fake News Detection with Graph Neural Networks, 2022, Proceedings of the ACM Web Conference 2022
  • Independence Promoted Graph Disentangled Networks, 2020, Proceedings of the AAAI Conference on Artificial Intelligence

Their frequent coauthors include Liang Wang, Qiang Liu, Yanqiao Zhu, Zeyu Cui, and Mengqi Zhang.

Shu Wu's work has been published in several venues repeatedly, with a particular focus on:

  • arXiv (Cornell University)
  • IEEE Transactions on Knowledge and Data Engineering
  • Proceedings of the AAAI Conference on Artificial Intelligence
  • Pattern Recognition
  • Machine Intelligence Research

With over 200 publications, Shu Wu's research integrates areas such as graph neural networks, recommendation systems, misinformation detection, and computational techniques for complex networks and domain adaptation. Their scholarly output contributes to evolving methods and understanding within artificial intelligence and computer science broadly.

Best Publications

  • Session-Based Recommendation with Graph Neural Networks

    Shu Wu;Yuyuan Tang;Yanqiao Zhu;Liang Wang

  • Predicting the next location: a recurrent model with spatial and temporal contexts

    Qiang Liu;Shu Wu;Liang Wang;Tieniu Tan

  • Graph Contrastive Learning with Adaptive Augmentation

    Yanqiao Zhu;Yichen Xu;Feng Yu;Qiang Liu

  • A Dynamic Recurrent Model for Next Basket Recommendation

    Feng Yu;Qiang Liu;Shu Wu;Liang Wang

  • A convolutional approach for misinformation identification

    Feng Yu;Qiang Liu;Shu Wu;Liang Wang

  • Deep Graph Contrastive Representation Learning.

    Yanqiao Zhu;Yichen Xu;Feng Yu;Qiang Liu

  • Every Document Owns Its Structure: Inductive Text Classification via Graph Neural Networks

    Yufeng Zhang;Xueli Yu;Zeyu Cui;Shu Wu

  • A Comprehensive Survey on Cross-modal Retrieval

    Kaiye Wang;Qiyue Yin;Wei Wang;Shu Wu

  • Mining Latent Structures for Multimedia Recommendation

    Jinghao Zhang;Yanqiao Zhu;Qiang Liu;Shu Wu

  • Mining Latent Structures for Multimedia Recommendation

    Jinghao Zhang;Yanqiao Zhu;Qiang Liu;Shu Wu

  • Context-Aware Sequential Recommendation

    Qiang Liu;Shu Wu;Diyi Wang;Zhaokang Li

  • Dynamic Graph Neural Networks for Sequential Recommendation.

    Mengqi Zhang;Shu Wu;Xueli Yu;Liang Wang

  • TAGNN: Target Attentive Graph Neural Networks for Session-based Recommendation

    Feng Yu;Yanqiao Zhu;Qiang Liu;Shu Wu

  • Information-Theoretic Outlier Detection for Large-Scale Categorical Data

    Shu Wu;Shengrui Wang

  • MV-RNN: A Multi-View Recurrent Neural Network for Sequential Recommendation

    Qiang Cui;Shu Wu;Qiang Liu;Wen Zhong

  • Fi-GNN: Modeling Feature Interactions via Graph Neural Networks for CTR Prediction

    Zekun Li;Zeyu Cui;Shu Wu;Xiaoyu Zhang

  • Graph Contrastive Learning with Adaptive Augmentation

    Yanqiao Zhu;Yichen Xu;Feng Yu;Qiang Liu

  • DeepStyle: Learning User Preferences for Visual Recommendation

    Qiang Liu;Shu Wu;Liang Wang

  • A Convolutional Click Prediction Model

    Qiang Liu;Feng Yu;Shu Wu;Liang Wang

  • Multi-view clustering via pairwise sparse subspace representation

    Qiyue Yin;Shu Wu;Ran He;Liang Wang

  • Hierarchical Graph Convolutional Networks for Semi-supervised Node Classification.

    Fenyu Hu;Yanqiao Zhu;Shu Wu;Liang Wang

  • Latent Structure Mining With Contrastive Modality Fusion for Multimedia Recommendation

    Unknown

  • Dressing as a Whole: Outfit Compatibility Learning Based on Node-wise Graph Neural Networks

    Zeyu Cui;Zekun Li;Shu Wu;Xiao-Yu Zhang

  • Unified subspace learning for incomplete and unlabeled multi-view data

    Qiyue Yin;Shu Wu;Liang Wang

  • Multi-Behavioral Sequential Prediction with Recurrent Log-Bilinear Model

    Qiang Liu;Shu Wu;Liang Wang

  • Dynamic Graph Collaborative Filtering

    Xiaohan Li;Mengqi Zhang;Shu Wu;Zheng Liu

  • Fi-GNN: Modeling Feature Interactions via Graph Neural Networks for CTR Prediction

    Zekun Li;Zeyu Cui;Shu Wu;Xiaoyu Zhang

Frequent Co-Authors

Liang Wang
Liang Wang Chinese Academy of Sciences
Tieniu Tan
Tieniu Tan Chinese Academy of Sciences
Daqing Zhang
Daqing Zhang Peking University
Xing Xie
Xing Xie Microsoft Research Asia (China)
Ran He
Ran He Chinese Academy of Sciences
Philip S. Yu
Philip S. Yu University of Illinois at Chicago
Shuhui Wang
Shuhui Wang Chinese Academy of Sciences
Ke Xu
Ke Xu Beihang University
Yongzhen Huang
Yongzhen Huang Chinese Academy of Sciences
Yan Huang
Yan Huang Chinese Academy of Sciences

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