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Computer Science
Singapore
2026

D-Index & Metrics

Computer Science

D-Index
123
Citations
71014
World Ranking
127
National Ranking
4

Tat-Seng Chua 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 Tat-Seng Chua 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: 713 publications — 98th percentile

98% of scientists in this discipline score the same or lower.

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

Tat-Seng Chua 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 Tat-Seng Chua 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: 123 D-Index — 99th percentile

99% of scientists in this discipline score the same or lower.

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

Research.com Recognitions

  • 2026 - Research.com Computer Science in Singapore Leader Award
  • 2025 - Research.com Computer Science in Singapore Leader Award
  • 2023 - Research.com Computer Science in Singapore Leader Award
  • 2022 - Research.com Computer Science in Singapore Leader Award

Overview

Tat-Seng Chua is affiliated with the National University of Singapore in Singapore. Their work primarily spans across the field of Computer Science, with a significant focus on several subfields including Artificial Intelligence, Computer Vision and Pattern Recognition, Information Systems, Management Science and Operations Research, and Computer Networks and Communications.

Their research contributions cover a broad range of topics within these areas. Main topics include:

  • Topic Modeling
  • Multimodal Machine Learning Applications
  • Recommender Systems and Techniques
  • Natural Language Processing Techniques
  • Advanced Graph Neural Networks
  • Domain Adaptation and Few-Shot Learning
  • Advanced Image and Video Retrieval Techniques

Tat-Seng Chua has published extensively, with notable recent papers including:

  • MGAT: Multimodal Graph Attention Network for Recommendation, 2020, Information Processing & Management
  • Self-Supervised Learning for Multimedia Recommendation, 2022, IEEE Transactions on Multimedia
  • Retrieving and Reading: A Comprehensive Survey on Open-domain Question Answering, 2021, arXiv (Cornell University)
  • Affective Image Content Analysis: Two Decades Review and New Perspectives, 2021, IEEE Transactions on Pattern Analysis and Machine Intelligence
  • Causal Attention for Interpretable and Generalizable Graph Classification, 2022, Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

In terms of publication venues, their work has appeared frequently in:

  • arXiv (Cornell University)
  • Proceedings of the AAAI Conference on Artificial Intelligence
  • ACM Transactions on Information Systems
  • IEEE Transactions on Multimedia
  • IEEE Transactions on Pattern Analysis and Machine Intelligence

Collaboration is notable in their career, with frequent co-authors including:

  • Fuli Feng
  • Xiang Wang
  • Xiangnan He
  • Wenqiang Lei
  • Yunshan Ma

Through this broad and multidisciplinary approach, Tat-Seng Chua's research has contributed to areas such as multimodal data analysis, recommendation systems, and graph-based learning methods, applying advanced techniques in artificial intelligence and machine learning.

Best Publications

  • Neural Collaborative Filtering

    Xiangnan He;Lizi Liao;Hanwang Zhang;Liqiang Nie

  • Neural Graph Collaborative Filtering

    Xiang Wang;Xiangnan He;Meng Wang;Fuli Feng

  • NUS-WIDE: a real-world web image database from National University of Singapore

    Tat-Seng Chua;Jinhui Tang;Richang Hong;Haojie Li

  • SCA-CNN: Spatial and Channel-Wise Attention in Convolutional Networks for Image Captioning

    Long Chen;Hanwang Zhang;Jun Xiao;Liqiang Nie

  • KGAT: Knowledge Graph Attention Network for Recommendation

    Xiang Wang;Xiangnan He;Yixin Cao;Meng Liu

  • Toward Scalable Systems for Big Data Analytics: A Technology Tutorial

    Han Hu;Yonggang Wen;Tat-Seng Chua;Xuelong Li

  • Neural Factorization Machines for Sparse Predictive Analytics

    Xiangnan He;Tat-Seng Chua

  • Meta-Transfer Learning for Few-Shot Learning

    Qianru Sun;Yaoyao Liu;Tat-Seng Chua;Bernt Schiele

  • Fast Matrix Factorization for Online Recommendation with Implicit Feedback

    Xiangnan He;Hanwang Zhang;Min-Yen Kan;Tat-Seng Chua

  • Disentangled Graph Collaborative Filtering

    Xiang Wang;Hongye Jin;An Zhang;Xiangnan He

  • Attentional Factorization Machines: Learning the Weight of Feature Interactions via Attention Networks

    Jun Xiao;Hao Ye;Xiangnan He;Hanwang Zhang

  • Attentive Collaborative Filtering: Multimedia Recommendation with Item- and Component-Level Attention

    Jingyuan Chen;Hanwang Zhang;Xiangnan He;Liqiang Nie

  • Explainable Reasoning over Knowledge Graphs for Recommendation

    Xiang Wang;Dingxian Wang;Canran Xu;Xiangnan He

  • Temporal Relational Ranking for Stock Prediction

    Fuli Feng;Xiangnan He;Xiang Wang;Cheng Luo

  • Unifying Knowledge Graph Learning and Recommendation: Towards a Better Understanding of User Preferences

    Yixin Cao;Xiang Wang;Xiangnan He;Zikun Hu

  • Visual Translation Embedding Network for Visual Relation Detection

    Hanwang Zhang;Zawlin Kyaw;Shih-Fu Chang;Tat-Seng Chua

  • MMGCN: Multi-modal Graph Convolution Network for Personalized Recommendation of Micro-video

    Yinwei Wei;Xiang Wang;Liqiang Nie;Xiangnan He

  • NAIS: Neural Attentive Item Similarity Model for Recommendation

    Xiangnan He;Zhankui He;Jingkuan Song;Zhenguang Liu

  • Hierarchical spatio-temporal context modeling for action recognition

    Ju Sun;Xiao Wu;Shuicheng Yan;Loong-Fah Cheong

  • Neural Sparse Voxel Fields

    Lingjie Liu;Jiatao Gu;Kyaw Zaw Lin;Tat-Seng Chua

  • Topical word embeddings

    Yang Liu;Zhiyuan Liu;Tat-Seng Chua;Maosong Sun

  • Tour the world: Building a web-scale landmark recognition engine

    Yan-Tao Zheng;Ming Zhao;Yang Song;Hartwig Adam

Frequent Co-Authors

Xiangnan He
Xiangnan He University of Science and Technology of China
Meng Wang
Meng Wang Hefei University of Technology
Liqiang Nie
Liqiang Nie Shandong University
Hanwang Zhang
Hanwang Zhang Nanyang Technological University
Richang Hong
Richang Hong Hefei University of Technology
Zheng-Jun Zha
Zheng-Jun Zha University of Science and Technology of China
Jinhui Tang
Jinhui Tang Nanjing University of Science and Technology
Shuicheng Yan
Shuicheng Yan National University of Singapore
Min-Yen Kan
Min-Yen Kan National University of Singapore
Fuli Feng
Fuli Feng University of Science and Technology of China

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