World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
35
Citations
9290
World Ranking
11453
National Ranking
345

Bogdan Gabrys 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 Bogdan Gabrys 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: 228 publications — 56th percentile

56% of scientists in this discipline score the same or lower.

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

Bogdan Gabrys 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 Bogdan Gabrys 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: 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

Bogdan Gabrys is affiliated with the University of Technology Sydney in Australia. Their research primarily spans the field of Computer Science with a focus on Artificial Intelligence, Statistical and Nonlinear Physics, Molecular Biology, Control and Systems Engineering, and Computer Vision and Pattern Recognition.

The main topics addressed in their work include:

  • Complex Network Analysis Techniques
  • Machine Learning and Data Classification
  • Data Stream Mining Techniques
  • Advanced Graph Neural Networks
  • Neural Networks and Applications
  • Machine Learning and Algorithms
  • Viral Infectious Diseases and Gene Expression in Insects

Gabrys has published numerous papers, including recent works such as:

  • "Foundations and Modeling of Dynamic Networks Using Dynamic Graph Neural Networks: A Survey" (2021, IEEE Access)
  • "NATS-Bench: Benchmarking NAS Algorithms for Architecture Topology and Size" (2021, IEEE Transactions on Pattern Analysis and Machine Intelligence)
  • "Applications of machine learning in antibody discovery, process development, manufacturing and formulation: Current trends, challenges, and opportunities" (2024, Computers & Chemical Engineering)
  • "Toward Digital Twin Oriented Modeling of Complex Networked Systems and Their Dynamics: A Comprehensive Survey" (2022, IEEE Access)
  • "Forty years of computers & chemical engineering: A bibliometric analysis" (2020, Computers & Chemical Engineering)

Their frequent coauthors include:

  • Katarzyna Musiał (43 co-authored works)
  • Thanh Tung Khuat (23 co-authored works)
  • David Jacob Kedziora (16 co-authored works)
  • Mingshan Jia (12 co-authored works)
  • Jiaqi Wen (7 co-authored works)

Gabrys publishes regularly in venues such as:

  • arXiv (Cornell University) with 28 publications
  • IEEE Access with 6 publications
  • PLoS ONE with 5 publications
  • Applied Soft Computing with 3 publications
  • Computers & Chemical Engineering with 2 publications

Best Publications

  • Data-driven Soft Sensors in the process industry

    Petr Kadlec;Bogdan Gabrys;Sibylle Strandt

  • Classifier selection for majority voting

    Dymitr Ruta;Bogdan Gabrys

  • Review of adaptation mechanisms for data-driven soft sensors

    Petr Kadlec;Ratko Grbić;Bogdan Gabrys

  • An Overview of Classifier Fusion Methods

    Dymitr Ruta;Bogdan Gabrys

  • General fuzzy min-max neural network for clustering and classification

    B. Gabrys;A. Bargiela

  • Metalearning: a survey of trends and technologies

    Christiane Lemke;Marcin Budka;Bogdan Gabrys

  • Meta-learning for time series forecasting and forecast combination

    Christiane Lemke;Bogdan Gabrys

  • Foundations and Modeling of Dynamic Networks Using Dynamic Graph Neural Networks: A Survey

    Joakim Skarding;Bogdan Gabrys;Katarzyna Musial

  • Local learning‐based adaptive soft sensor for catalyst activation prediction

    Petr Kadlec;Bogdan Gabrys

  • The security challenges in the IoT enabled cyber-physical systems and opportunities for evolutionary computing & other computational intelligence

    Hongmei He;Carsten Maple;Tim Watson;Ashutosh Tiwari

  • NATS-Bench: Benchmarking NAS Algorithms for Architecture Topology and Size.

    Xuanyi Dong;Lu Liu;Katarzyna Musial;Bogdan Gabrys

  • Genetic algorithms in classifier fusion

    Bogdan Gabrys;Dymitr Ruta

  • Multi-level Graph Convolutional Networks for Cross-platform Anchor Link Prediction

    Hongxu Chen;Hongzhi Yin;Xiangguo Sun;Tong Chen

  • Next challenges for adaptive learning systems

    Indre Zliobaite;Albert Bifet;Mohamed Gaber;Bogdan Gabrys

  • A Theoretical Analysis of the Limits of Majority Voting Errors for Multiple Classifier Systems

    Dymitr Ruta;Bogdan Gabrys

  • Analysis of the Correlation Between Majority Voting Error and the Diversity Measures in Multiple Classifier Systems

    Dymitr Ruta;Bogdan Gabrys

  • Adaptive Preprocessing for Streaming Data

    Indre Zliobaite;Bogdan Gabrys

  • Neuro-fuzzy approach to processing inputs with missing values in pattern recognition problems

    Bogdan Gabrys

  • Combining labelled and unlabelled data in the design of pattern classification systems

    Bogdan Gabrys;Lina Petrakieva

  • Application of the Evolutionary Algorithms for Classifier Selection in Multiple Classifier Systems with Majority Voting

    Dymitr Ruta;Bogdan Gabrys

  • Adaptive community detection incorporating topology and content in social networks

    Meng Qin;Di Jin;Kai Lei;Bogdan Gabrys

Frequent Co-Authors

Xuanyi Dong
Xuanyi Dong Google (United States)
Emilio Corchado
Emilio Corchado University of Salamanca
Yaochu Jin
Yaochu Jin Westlake University
Hongzhi Yin
Hongzhi Yin University of Queensland
Mohamed Medhat Gaber
Mohamed Medhat Gaber Birmingham City University
José M. Merigó
José M. Merigó University of Technology Sydney
Ashutosh Tiwari
Ashutosh Tiwari University of Utah
Darek Ceglarek
Darek Ceglarek University of Warwick
Stefan K. Bohlander
Stefan K. Bohlander University of Auckland
Alexander M. Korsunsky
Alexander M. Korsunsky University of Oxford

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