World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
54
Citations
11639
World Ranking
4567
National Ranking
134

Michael Small 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 Michael Small 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: 334 publications — 79th percentile

79% of scientists in this discipline score the same or lower.

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

Michael Small 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 Michael Small 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: 54 D-Index — 69th percentile

69% of scientists in this discipline score the same or lower.

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

Overview

Michael Small is affiliated with the University of Western Australia in Australia. Their research spans the intersection of computer science and physics, with a substantial focus on areas such as statistical and nonlinear physics as well as artificial intelligence.

The main fields of study in their work include:

  • Computer Science
  • Physics and Astronomy

Within these fields, key subfields addressed by Michael Small are:

  • Statistical and Nonlinear Physics
  • Artificial Intelligence
  • Cognitive Neuroscience
  • Computer Networks and Communications
  • Molecular Biology

Their primary research topics cover:

  • Complex Network Analysis Techniques
  • Neural Networks and Reservoir Computing
  • Neural dynamics and brain function
  • Opinion Dynamics and Social Influence
  • Nonlinear Dynamics and Pattern Formation
  • Neural Networks and Applications
  • Chaos control and synchronization

Michael Small has published extensively, with frequent appearances in key venues such as:

  • Chaos An Interdisciplinary Journal of Nonlinear Science
  • arXiv (Cornell University)
  • Physica A Statistical Mechanics and its Applications
  • Physical review. E
  • SSRN Electronic Journal

Recent papers by Michael Small include:

  • Modelling Strong Control Measures for Epidemic Propagation With Networks-A COVID-19 Case Study, 2020, IEEE Access
  • Selecting embedding delays: An overview of embedding techniques and a new method using persistent homology, 2023, Chaos An Interdisciplinary Journal of Nonlinear Science
  • Searching for Key Cycles in a Complex Network, 2023, Physical Review Letters
  • A social communication model based on simplicial complexes, 2020, Physics Letters A
  • Growing networks with communities: A distributive link model, 2020, Chaos An Interdisciplinary Journal of Nonlinear Science

In their collaborative work, Michael Small frequently coauthors with:

  • Shannon D. Algar (18 collaborations)
  • Débora Corrêa (17 collaborations)
  • Thomas Stemler (17 collaborations)
  • Thomas Jüngling (15 collaborations)
  • Thomas Lymburn (11 collaborations)

Best Publications

  • Complex network from pseudoperiodic time series: Topology versus Dynamics

    J. Zhang;Michael Small

  • Superfamily phenomena and motifs of networks induced from time series

    Xiaoke Xu;Jie Zhang;Michael Small

  • Applied nonlinear time series analysis : applications in physics, physiology and finance

    Michael Small

  • RECURRENCE-BASED TIME SERIES ANALYSIS BY MEANS OF COMPLEX NETWORK METHODS

    Reik V. Donner;Michael Small;Jonathan F. Donges;Jonathan F. Donges;Norbert Marwan

  • Complex network analysis of time series

    Zhong Ke Gao;Michael Small;Jürgen Kurths;Jürgen Kurths;Jürgen Kurths

  • Epidemic dynamics on scale-free networks with piecewise linear infectivity and immunization.

    Xinchu Fu;Michael Small;David M. Walker;Haifeng Zhang

  • The impact of awareness on epidemic spreading in networks

    Qingchu Wu;Xinchu Fu;Michael Small;Xin-Jian Xu

  • Dynamic versus static biomarkers in cancer immune checkpoint blockade: unravelling complexity.

    W. Joost Lesterhuis;Anthony Bosco;Michael J. Millward;Michael J. Millward;Michael Small;Michael Small

  • Time lagged ordinal partition networks for capturing dynamics of continuous dynamical systems.

    Michael McCullough;Michael Small;Thomas Stemler;Herbert Ho-Ching Iu

  • Characterizing pseudoperiodic time series through the complex network approach

    Jie Zhang;Junfeng Sun;Xiaodong Luo;Kai Zhang

  • Surrogate Test for Pseudoperiodic Time Series Data

    Michael Small;Michael Small;Dejin Yu;Robert G. Harrison

  • Hub nodes inhibit the outbreak of epidemic under voluntary vaccination

    Haifeng Zhang;Haifeng Zhang;Haifeng Zhang;Jie Zhang;Changsong Zhou;Michael Small

  • Basin of Attraction Determines Hysteresis in Explosive Synchronization

    Yong Zou;Yong Zou;Tiago Pereira;Michael Small;Zonghua Liu

  • Synchronization of chaotic systems and their machine-learning models.

    Tongfeng Weng;Huijie Yang;Changgui Gu;Jie Zhang

  • Optimal embedding parameters: A modelling paradigm

    Michael Small;Chi Kong Tse

  • Efficient implementation of the gaussian kernel algorithm in estimating invariants and noise level from noisy time series data

    Dejin Yu;Michael Small;Robert G. Harrison;Cees Diks

  • Is breathing in infants chaotic? Dimension estimates for respiratory patterns during quiet sleep

    Michael Small;Kevin Judd;M. Lowe;S. Stick

  • Super-spreaders and the rate of transmission of the SARS virus

    Michael Small;Chi Kong Tse;David M. Walker

  • Applying the method of surrogate data to cyclic time series

    Michael Small;C.K. Tse

  • Complex network structure of musical compositions: Algorithmic generation of appealing music

    Xiao Fan Liu;Chi Kong Tse;Michael Small

  • Detecting determinism in time series: the method of surrogate data

    M. Small;C.K. Tse

Frequent Co-Authors

Chi K. Tse
Chi K. Tse City University of Hong Kong
Hai-Feng Zhang
Hai-Feng Zhang Chinese Academy of Sciences
Guanrong Chen
Guanrong Chen City University of Hong Kong
Pan Hui
Pan Hui Hong Kong University of Science and Technology
Herbert Ho-Ching Iu
Herbert Ho-Ching Iu University of Western Australia
Jun-an Lu
Jun-an Lu Wuhan University
Hugh P. C. Robinson
Hugh P. C. Robinson University of Cambridge
Bing-Hong Wang
Bing-Hong Wang University of Science and Technology of China
Jürgen Kurths
Jürgen Kurths Potsdam Institute for Climate Impact Research
Wenwu Yu
Wenwu Yu Southeast University

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