World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
74
Citations
22121
World Ranking
1494
National Ranking
202

Richang Hong 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 Richang Hong 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: 296 publications — 73rd percentile

73% of scientists in this discipline score the same or lower.

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

Richang Hong 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 Richang Hong 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: 74 D-Index — 90th percentile

90% of scientists in this discipline score the same or lower.

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

Overview

Richang Hong is affiliated with Hefei University of Technology in China and has contributed extensively to the field of computer science with a focus on computer vision, artificial intelligence, and information systems. Their work spans multiple specialized areas including advanced graph neural networks, multimodal machine learning applications, and recommender systems, among others.

The main fields of study covered by their research include:

  • Computer Science

Their research further delves into subfields such as:

  • Computer Vision and Pattern Recognition
  • Artificial Intelligence
  • Information Systems
  • Social Psychology
  • Signal Processing

Richang Hong's scholarly contributions address diverse topics including:

  • Multimodal Machine Learning Applications
  • Domain Adaptation and Few-Shot Learning
  • Advanced Image and Video Retrieval Techniques
  • Recommender Systems and Techniques
  • Advanced Graph Neural Networks
  • Human Pose and Action Recognition
  • Topic Modeling

Frequent co-authors in their work include:

  • Meng Wang
  • Yanrong Guo
  • Le Wu
  • Shijie Hao
  • Zhao Zhang

Publications by Richang Hong appear regularly in key venues such as:

  • arXiv (Cornell University)
  • IEEE Transactions on Multimedia
  • IEEE Transactions on Computational Social Systems
  • IEEE Transactions on Circuits and Systems for Video Technology
  • Proceedings of the 30th ACM International Conference on Multimedia

Selected recent papers include:

  • Revisiting Graph Based Collaborative Filtering: A Linear Residual Graph Convolutional Network Approach, 2020, Proceedings of the AAAI Conference on Artificial Intelligence
  • DiffNet++: A Neural Influence and Interest Diffusion Network for Social Recommendation, 2020, IEEE Transactions on Knowledge and Data Engineering
  • Exploiting Subspace Relation in Semantic Labels for Cross-Modal Hashing, 2020, IEEE Transactions on Knowledge and Data Engineering
  • A Review-aware Graph Contrastive Learning Framework for Recommendation, 2022, Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval
  • Deep Color Consistent Network for Low-Light Image Enhancement, 2022, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Best Publications

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

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

  • Revisiting Graph Based Collaborative Filtering: A Linear Residual Graph Convolutional Network Approach

    Lei Chen;Le Wu;Richang Hong;Kun Zhang

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

    Yinwei Wei;Xiang Wang;Liqiang Nie;Xiangnan He

  • Crowded Scene Analysis: A Survey

    Teng Li;Huan Chang;Meng Wang;Bingbing Ni

  • A Neural Influence Diffusion Model for Social Recommendation

    Le Wu;Peijie Sun;Yanjie Fu;Richang Hong

  • Unified Video Annotation via Multigraph Learning

    Meng Wang;Xian-Sheng Hua;Richang Hong;Jinhui Tang

  • Multi-cue Correlation Filters for Robust Visual Tracking

    Ning Wang;Wengang Zhou;Qi Tian;Richang Hong

  • Multiple feature hashing for real-time large scale near-duplicate video retrieval

    Jingkuan Song;Yi Yang;Zi Huang;Heng Tao Shen

  • DiffNet++: A Neural Influence and Interest Diffusion Network for Social Recommendation

    Le Wu;Junwei Li;Peijie Sun;Richang Hong

  • Deep Item-based Collaborative Filtering for Top-N Recommendation

    Feng Xue;Xiangnan He;Xiang Wang;Jiandong Xu

  • Beyond Distance Measurement: Constructing Neighborhood Similarity for Video Annotation

    Meng Wang;Xian-Sheng Hua;Jinhui Tang;Richang Hong

  • Event Driven Web Video Summarization by Tag Localization and Key-Shot Identification

    Meng Wang;R. Hong;Guangda Li;Zheng-Jun Zha

  • Adaptive Transfer Network for Cross-Domain Person Re-Identification

    Jiawei Liu;Zheng-Jun Zha;Di Chen;Richang Hong

  • Point-of-Interest Recommendations: Learning Potential Check-ins from Friends

    Huayu Li;Yong Ge;Richang Hong;Hengshu Zhu

  • Deep Representation Learning With Part Loss for Person Re-Identification

    Hantao Yao;Shiliang Zhang;Richang Hong;Yongdong Zhang

  • Image annotation by kNN-sparse graph-based label propagation over noisily tagged web images

    Jinhui Tang;Richang Hong;Shuicheng Yan;Tat-Seng Chua

  • Self-Supervised Video Hashing With Hierarchical Binary Auto-Encoder

    Jingkuan Song;Hanwang Zhang;Xiangpeng Li;Lianli Gao

  • Camera Constraint-Free View-Based 3-D Object Retrieval

    Yue Gao;Jinhui Tang;Richang Hong;Shuicheng Yan

  • Estimation-Action-Reflection: Towards Deep Interaction Between Conversational and Recommender Systems

    Wenqiang Lei;Xiangnan He;Yisong Miao;Qingyun Wu

  • Inferring semantic concepts from community-contributed images and noisy tags

    Jinhui Tang;Shuicheng Yan;Richang Hong;Guo-Jun Qi

  • Attentive Group Recommendation

    Da Cao;Xiangnan He;Lianhai Miao;Yahui An

  • Spectral-Spatial Constraint Hyperspectral Image Classification

    Rongrong Ji;Yue Gao;Richang Hong;Qiong Liu

Frequent Co-Authors

Meng Wang
Meng Wang Hefei University of Technology
Tat-Seng Chua
Tat-Seng Chua National University of Singapore
Qi Tian
Qi Tian Huawei Technologies (China)
Shuicheng Yan
Shuicheng Yan National University of Singapore
Yong Ge
Yong Ge University of Arizona
Jinhui Tang
Jinhui Tang Nanjing University of Science and Technology
Liqiang Nie
Liqiang Nie Shandong University
Zheng-Jun Zha
Zheng-Jun Zha University of Science and Technology of China
Xiangnan He
Xiangnan He University of Science and Technology of China
Hanwang Zhang
Hanwang Zhang Nanyang Technological University

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