World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
35
Citations
4781
World Ranking
11763
National Ranking
4811

Jingrui He 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 Jingrui He 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: 250 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: 560 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: 424 scientists 242–251 publications: 408 scientists 252–261 publications: 378 scientists 262–271 publications: 300 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: 231 publications — 57th percentile

57% of scientists in this discipline score the same or lower.

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

Jingrui He 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 Jingrui He sits on this spectrum.

30–31 D-Index: 879 scientists 32–33 D-Index: 984 scientists 34–35 D-Index: 918 scientists 36–37 D-Index: 990 scientists 38–39 D-Index: 969 scientists 40–41 D-Index: 907 scientists 42–43 D-Index: 821 scientists 44–45 D-Index: 765 scientists 46–47 D-Index: 689 scientists 48–49 D-Index: 543 scientists 50–51 D-Index: 543 scientists 52–53 D-Index: 517 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: 35 D-Index — 20th percentile

20% of scientists in this discipline score the same or lower.

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

Overview

Jingrui He is affiliated with the University of Illinois at Urbana-Champaign in the United States. Their research primarily spans the field of Computer Science, with a particular focus on areas such as Artificial Intelligence, Computer Vision and Pattern Recognition, Management Science and Operations Research, Information Systems, and Statistical and Nonlinear Physics.

Their work covers several key topics including Advanced Graph Neural Networks, Domain Adaptation and Few-Shot Learning, Advanced Bandit Algorithms Research, Privacy-Preserving Technologies in Data, Complex Network Analysis Techniques, Multimodal Machine Learning Applications, and Recommender Systems and Techniques.

Jingrui He has contributed to numerous publications, with frequent appearances in venues such as arXiv (Cornell University), Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Proceedings of the AAAI Conference on Artificial Intelligence, Proceedings of the 31st ACM International Conference on Information & Knowledge Management, and the 2022 IEEE International Conference on Big Data (Big Data).

Their recent papers include:

  • "Airborne hyperspectral imaging of cover crops through radiative transfer process-guided machine learning," 2022, Remote Sensing of Environment
  • "Comprehensive Fair Meta-learned Recommender System," 2022, Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
  • "High-Order Structure Exploration on Massive Graphs," 2021, ACM Transactions on Knowledge Discovery from Data
  • "A Visual Analytics Framework for Explaining and Diagnosing Transfer Learning Processes," 2020, IEEE Transactions on Visualization and Computer Graphics
  • "Augmentations in Hypergraph Contrastive Learning: Fabricated and Generative," 2022, PubMed

Jingrui He has collaborated frequently with other researchers. Their most common co-authors include Hanghang Tong, Dongqi Fu, Tianxin Wei, Lecheng Zheng, and Yada Zhu.

Best Publications

  • Manifold-ranking based image retrieval

    Jingrui He;Mingjing Li;Hong-Jiang Zhang;Hanghang Tong

  • Comparing Random Forest with Logistic Regression for Predicting Class-Imbalanced Civil War Onset Data

    David Muchlinski;David Siroky;Jingrui He;Matthew Kocher

  • DEMO-Net: Degree-specific Graph Neural Networks for Node and Graph Classification

    Jun Wu;Jingrui He;Jiejun Xu

  • Classification of digital photos taken by photographers or home users

    Hanghang Tong;Mingjing Li;Hong-Jiang Zhang;Jingrui He

  • Generalized Manifold-Ranking-Based Image Retrieval

    Jingrui He;Mingjing Li;Hong-Jiang Zhang;Hanghang Tong

  • A Graph-based Framework for Multi-Task Multi-View Learning

    Jingrui He;Rick Lawrence

  • Graph based multi-modality learning

    Hanghang Tong;Jingrui He;Mingjing Li;Changshui Zhang

  • Improving traffic prediction with tweet semantics

    Jingrui He;Wei Shen;Phani Divakaruni;Laura Wynter

  • Conclusion and Future Directions

    Jingrui He

  • InFoRM: Individual Fairness on Graph Mining

    Jian Kang;Jingrui He;Ross Maciejewski;Hanghang Tong

  • A Data-Driven Graph Generative Model for Temporal Interaction Networks

    Dawei Zhou;Lecheng Zheng;Jiawei Han;Jingrui He

  • Diversified ranking on large graphs: an optimization viewpoint

    Hanghang Tong;Jingrui He;Zhen Wen;Ravi Konuru

  • Nearest-Neighbor-Based Active Learning for Rare Category Detection

    Jingrui He;Jaime G. Carbonell

  • Multi-view transfer learning with a large margin approach

    Dan Zhang;Jingrui He;Yan Liu;Luo Si

  • Mean version space: a new active learning method for content-based image retrieval

    Jingrui He;Hanghang Tong;Mingjing Li;Hong-Jiang Zhang

  • A Local Algorithm for Structure-Preserving Graph Cut

    Dawei Zhou;Si Zhang;Mehmet Yigit Yildirim;Scott Alcorn

  • Learning No-Reference Quality Metric by Examples

    Hanghang Tong;Mingjing Li;Hong-Jiang Zhang;Changshui Zhang

  • SPARC: Self-Paced Network Representation for Few-Shot Rare Category Characterization

    Dawei Zhou;Jingrui He;Hongxia Yang;Wei Fan

  • Method and System for Wafer Quality Predictive Modeling based on Multi-Source Information with Heterogeneous Relatedness

    Yada Zhu;Jingrui He;Robert Jeffrey Baseman

  • HiDDen: Hierarchical dense subgraph detection with application to financial fraud detection

    Si Zhang;Dawei Zhou;Mehmet Yigit Yildirim;Scott Alcorn

  • Graph-Based Rare Category Detection

    Jingrui He;Yan Liu;R. Lawrence

  • Augmentations in Hypergraph Contrastive Learning: Fabricated and Generative

    Unknown

Frequent Co-Authors

Hanghang Tong
Hanghang Tong University of Illinois at Urbana-Champaign
Changshui Zhang
Changshui Zhang Tsinghua University
Mingjing Li
Mingjing Li Microsoft (United States)
Jaime G. Carbonell
Jaime G. Carbonell Carnegie Mellon University
Nan Cao
Nan Cao Tongji University
Ross Maciejewski
Ross Maciejewski Arizona State University
Wei-Ying Ma
Wei-Ying Ma Tsinghua University
Yu Cheng
Yu Cheng Microsoft (United States)
Ching-Yung Lin
Ching-Yung Lin National Chi Nan University

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