World's Best Scientists 2026 revealed!
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Computer Science
Australia
2025

D-Index & Metrics

Computer Science

D-Index
71
Citations
35065
World Ranking
1730
National Ranking
236

Chengqi Zhang 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 Chengqi Zhang 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: 487 publications — 93rd percentile

93% of scientists in this discipline score the same or lower.

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

Chengqi Zhang 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 Chengqi Zhang 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: 71 D-Index — 88th percentile

88% 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

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

Overview

Chengqi Zhang is affiliated with the University of Technology Sydney in Australia and has a significant body of work primarily focused on computer science, with a particular emphasis on artificial intelligence. Their research spans multiple subfields including molecular biology, computer vision and pattern recognition, electrical and electronic engineering, and materials chemistry.

The scientist's main research topics include:

  • Topic Modeling
  • Multimodal Machine Learning Applications
  • Privacy-Preserving Technologies in Data
  • Magnetic Properties and Synthesis of Ferrites
  • Anomaly Detection Techniques and Applications
  • Advanced Graph Neural Networks
  • Domain Adaptation and Few-Shot Learning

Some of Chengqi Zhang's recent papers are:

  • "FedProto: Federated Prototype Learning across Heterogeneous Clients", 2022, Proceedings of the AAAI Conference on Artificial Intelligence
  • "Image super-resolution with an enhanced group convolutional neural network", 2022, Neural Networks
  • "Bidirectional Spatial-Temporal Adaptive Transformer for Urban Traffic Flow Forecasting", 2022, IEEE Transactions on Neural Networks and Learning Systems
  • "Federated Learning on Non-IID Graphs via Structural Knowledge Sharing", 2023, Proceedings of the AAAI Conference on Artificial Intelligence
  • "A cross Transformer for image denoising", 2023, Information Fusion

Frequent collaborators in their work include Guodong Long, Jing Jiang (two distinct author profiles with substantial collaboration counts), Tianyi Zhou, and Rui Tang.

Chengqi Zhang's publications are frequently found in:

  • arXiv (Cornell University)
  • SSRN Electronic Journal
  • Proceedings of the AAAI Conference on Artificial Intelligence
  • IEEE Transactions on Knowledge and Data Engineering
  • Research Square (Research Square)

In addition to journal articles and conference papers, Zhang has contributed to book publications through Springer Science+Business Media, notably in volumes titled "Advances in Knowledge Discovery and Data Mining" published in 2022.

Best Publications

  • A Comprehensive Survey on Graph Neural Networks

    Zonghan Wu;Shirui Pan;Fengwen Chen;Guodong Long

  • Graph WaveNet for Deep Spatial-Temporal Graph Modeling

    Zonghan Wu;Shirui Pan;Guodong Long;Jing Jiang

  • Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networks

    Zonghan Wu;Shirui Pan;Guodong Long;Jing Jiang

  • Adversarially regularized graph autoencoder for graph embedding

    Shirui Pan;Ruiqi Hu;Guodong Long;Jing Jiang

  • DiSAN: Directional Self-Attention Network for RNN/CNN-free Language Understanding

    Tao Shen;Tianyi Zhou;Guodong Long;Jing Jiang

  • Data preparation for data mining

    Shichao Zhang;Chengqi Zhang;Qiang Yang

  • Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

    Longbing Cao;Chengqi Zhang;Thorsten Joachims;Geoff Webb

  • Network Representation Learning: A Survey

    Daokun Zhang;Jie Yin;Xingquan Zhu;Chengqi Zhang

  • Association Rule Mining: Models and Algorithms

    Chengqi Zhang;Shichao Zhang

  • FedProto: Federated Prototype Learning across Heterogeneous Clients

    Unknown

  • Efficient mining of both positive and negative association rules

    Xindong Wu;Chengqi Zhang;Shichao Zhang

  • Attributed Graph Clustering: a Deep Attentional Embedding approach

    Chun Wang;Shirui Pan;Ruiqi Hu;Guodong Long

  • Tri-party deep network representation

    Shirui Pan;Jia Wu;Xingquan Zhu;Chengqi Zhang

  • Conference on Neural Information Processing Systems

    L Liu;T Zhou;Guodong Long;Jing Jiang

  • Learning Graph Embedding With Adversarial Training Methods

    Shirui Pan;Ruiqi Hu;Sai-Fu Fung;Guodong Long

  • Genetic algorithm-based strategy for identifying association rules without specifying actual minimum support

    Xiaowei Yan;Chengqi Zhang;Shichao Zhang

  • Dynamic Affinity Graph Construction for Spectral Clustering Using Multiple Features

    Zhihui Li;Feiping Nie;Xiaojun Chang;Yi Yang

  • Mining Both Positive and Negative Association Rules

    Xindong Wu;Chengqi Zhang;Shichao Zhang

  • Federated Learning for Open Banking

    Guodong Long;Yue Tan;Jing Jiang;Chengqi Zhang

  • Compound Rank- $k$ Projections for Bilinear Analysis

    Xiaojun Chang;Feiping Nie;Sen Wang;Yi Yang

  • Support vector machines based on K-means clustering for real-time business intelligence systems

    Jiaqi Wang;Xindong Wu;Chengqi Zhang

  • Proceedings of the 10th IEEE International Conference on Data Mining (ICDM)

    Geoffrey Webb;Bing Liu;Chengqi Zhang;Dimitrios Gunopulos

Frequent Co-Authors

Longbing Cao
Longbing Cao University of Technology Sydney
Guodong Long
Guodong Long University of Technology Sydney
Xingquan Zhu
Xingquan Zhu Florida Atlantic University
Jing Jiang
Jing Jiang Singapore Management University
Shirui Pan
Shirui Pan Griffith University
Jia Wu
Jia Wu Macquarie University
Xindong Wu
Xindong Wu Hefei University of Technology
Philip S. Yu
Philip S. Yu University of Illinois at Chicago
Peng Zhang
Peng Zhang Huazhong University of Science and Technology
Ivor W. Tsang
Ivor W. Tsang Agency for Science, Technology and Research

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