World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
66
Citations
19877
World Ranking
2299
National Ranking
1149

Xingquan Zhu 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 Xingquan Zhu 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: 338 publications — 80th percentile

80% of scientists in this discipline score the same or lower.

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

Xingquan Zhu 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 Xingquan Zhu 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: 66 D-Index — 84th percentile

84% of scientists in this discipline score the same or lower.

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

Overview

Xingquan Zhu is affiliated with Florida Atlantic University in the United States and has a research portfolio primarily within the field of Computer Science, contributing extensively to Artificial Intelligence, Information Systems, Computer Vision and Pattern Recognition, Signal Processing, and Parasitology.

Their research interests cover a variety of topics, including:

  • Advanced Graph Neural Networks
  • Data Mining Algorithms and Applications
  • Domain Adaptation and Few-Shot Learning
  • Complex Network Analysis Techniques
  • Anomaly Detection Techniques and Applications
  • Text and Document Classification Technologies
  • Time Series Analysis and Forecasting

Xingquan Zhu's recent papers illustrate a focus on deep learning methods, data augmentation, and security frameworks across multiple application domains. Notable recent publications include:

  • "Dropout vs. batch normalization: an empirical study of their impact to deep learning" (2020), published in Multimedia Tools and Applications
  • "Deep Learning for User Interest and Response Prediction in Online Display Advertising" (2020), published in Data Science and Engineering
  • "A survey and taxonomy of adversarial neural networks for text-to-image synthesis" (2020), published in Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery
  • "IoT Network Security: Threats, Risks, and a Data-Driven Defense Framework" (2020), published in IoT
  • "Deep learning data augmentation for Raman spectroscopy cancer tissue classification" (2021), published in Scientific Reports

The scientist frequently collaborates with peers including Xindong Wu, Youxi Wu, Yufei Tang, Yan Li, and Min Shi. These co-authors have worked alongside Zhu on numerous projects, reflecting ongoing research partnerships.

Publication venues where Zhu has contributed consistently include:

  • arXiv (Cornell University)
  • ACM Transactions on Knowledge Discovery from Data
  • IEEE Transactions on Knowledge and Data Engineering
  • Parasites & Vectors
  • Research Square (Research Square)

Xingquan Zhu has also authored a book titled Computational Data and Social Networks, published by Springer Science+Business Media in 2024. This work contributes to their academic output alongside peer-reviewed articles.

Best Publications

  • Data mining with big data

    Xindong Wu;Xingquan Zhu;Gong-Qing Wu;Wei Ding

  • Class noise vs. attribute noise: a quantitative study of their impacts

    Xingquan Zhu;Xindong Wu

  • Network Representation Learning: A Survey

    Daokun Zhang;Jie Yin;Xingquan Zhu;Chengqi Zhang

  • Dropout vs. batch normalization: an empirical study of their impact to deep learning

    Christian Garbin;Xingquan Zhu;Oge Marques

  • Machine Learning for Android Malware Detection Using Permission and API Calls

    Naser Peiravian;Xingquan Zhu

  • Tri-party deep network representation

    Shirui Pan;Jia Wu;Xingquan Zhu;Chengqi Zhang

  • MGAE: Marginalized Graph Autoencoder for Graph Clustering

    Chun Wang;Shirui Pan;Guodong Long;Xingquan Zhu

  • A survey on instance selection for active learning

    Yifan Fu;Xingquan Zhu;Bin Li

  • A unified framework for semantics and feature based relevance feedback in image retrieval systems

    Ye Lu;Chunhui Hu;Xingquan Zhu;HongJiang Zhang

  • Online Feature Selection with Streaming Features

    Xindong Wu;Kui Yu;Wei Ding;Hao Wang

  • Eliminating class noise in large datasets

    Xingquan Zhu;Xindong Wu;Qijun Chen

  • Relevance maximizing, iteration minimizing, relevance-feedback, content-based image retrieval (CBIR)

    Hong-Jiang Zhang;Zhong Su;Xingquan Zhu

  • Knowledge Discovery and Data Mining: Challenges and Realities

    Xingquan Zhu;Ian Davidson

  • ClassView: hierarchical video shot classification, indexing, and accessing

    Jianping Fan;A.K. Elmagarmid;Xingquan Zhu;W.G. Aref

  • Video data mining: semantic indexing and event detection from the association perspective

    Xingquan Zhu;Xindong Wu;A.K. Elmagarmid;Zhe Feng

  • Hashing Techniques: A Survey and Taxonomy

    Lianhua Chi;Xingquan Zhu

  • Active Learning From Stream Data Using Optimal Weight Classifier Ensemble

    Xingquan Zhu;Peng Zhang;Xiaodong Lin;Yong Shi

  • Combining proactive and reactive predictions for data streams

    Ying Yang;Xindong Wu;Xingquan Zhu

  • Bag Constrained Structure Pattern Mining for Multi-Graph Classification

    Jia Wu;Xingquan Zhu;Chengqi Zhang;Philip S. Yu

  • Mining With Noise Knowledge: Error-Aware Data Mining

    Xindong Wu;Xingquan Zhu

  • Unsupervised Domain Adaptive Graph Convolutional Networks

    Man Wu;Shirui Pan;Chuan Zhou;Xiaojun Chang

Frequent Co-Authors

Xindong Wu
Xindong Wu Hefei University of Technology
Chengqi Zhang
Chengqi Zhang Hong Kong Polytechnic University
Shirui Pan
Shirui Pan Griffith University
Jia Wu
Jia Wu Macquarie University
Jianping Fan
Jianping Fan University of North Carolina at Charlotte
Peng Zhang
Peng Zhang Huazhong University of Science and Technology
Ahmed K. Elmagarmid
Ahmed K. Elmagarmid Qatar Computing Research Institute
Yong Shi
Yong Shi Chinese Academy of Sciences
Xiangyang Xue
Xiangyang Xue Fudan University
Walid G. Aref
Walid G. Aref Purdue University West Lafayette

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