World's Best Scientists 2026 revealed!

D-Index & Metrics

Engineering and Technology

D-Index
41
Citations
5112
World Ranking
7112
National Ranking
1308

Ming Tang publication distribution in Engineering and Technology in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Engineering and Technology in 2026. The highlighted bar marks where Ming Tang sits on this spectrum.

38–47 publications: 20 scientists 48–57 publications: 35 scientists 58–67 publications: 96 scientists 68–77 publications: 135 scientists 78–87 publications: 190 scientists 88–97 publications: 259 scientists 98–107 publications: 283 scientists 108–117 publications: 369 scientists 118–127 publications: 341 scientists 128–137 publications: 386 scientists 138–147 publications: 372 scientists 148–157 publications: 457 scientists 158–167 publications: 415 scientists 168–177 publications: 407 scientists 178–187 publications: 421 scientists 188–197 publications: 378 scientists 198–207 publications: 403 scientists 208–217 publications: 317 scientists 218–227 publications: 346 scientists 228–237 publications: 321 scientists 238–247 publications: 260 scientists 248–257 publications: 280 scientists 258–267 publications: 240 scientists 268–277 publications: 214 scientists 278–287 publications: 242 scientists 288–297 publications: 203 scientists 298–307 publications: 166 scientists 308–317 publications: 154 scientists 318–327 publications: 175 scientists 328–337 publications: 159 scientists 338–347 publications: 99 scientists 348–357 publications: 131 scientists 358–367 publications: 106 scientists 368–377 publications: 118 scientists 378–387 publications: 97 scientists 388–397 publications: 108 scientists 398–407 publications: 82 scientists 408–417 publications: 71 scientists 418–427 publications: 64 scientists 428–437 publications: 55 scientists 438–447 publications: 54 scientists 448–457 publications: 60 scientists 458–467 publications: 47 scientists 468–477 publications: 40 scientists 478–487 publications: 30 scientists 488–497 publications: 29 scientists 498–507 publications: 38 scientists 508–517 publications: 40 scientists 518–527 publications: 32 scientists 528–537 publications: 23 scientists 538–547 publications: 28 scientists 548–557 publications: 23 scientists 558–567 publications: 19 scientists 568–577 publications: 16 scientists 578–587 publications: 17 scientists 588–597 publications: 18 scientists 598–607 publications: 22 scientists 608–617 publications: 15 scientists 618–627 publications: 9 scientists 628–637 publications: 11 scientists 638–647 publications: 21 scientists 648–657 publications: 12 scientists 658–667 publications: 9 scientists 668–677 publications: 11 scientists 678–687 publications: 9 scientists 688–697 publications: 6 scientists 698–707 publications: 14 scientists 708–717 publications: 7 scientists 718–727 publications: 8 scientists 728–737 publications: 10 scientists 738–747 publications: 9 scientists 748–757 publications: 5 scientists 758–767 publications: 5 scientists 768–777 publications: 11 scientists 778–787 publications: 7 scientists 788–797 publications: 2 scientists 798–803 publications: 4 scientists 804+ publications: 100 scientists
38 publications 804+

This scientist: 122 publications — 16th percentile

16% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 804 publications or more.

Ming Tang D-index placement in Engineering and Technology in 2026

The chart shows the D-index (discipline H-index) distribution of Engineering and Technology scientists ranked by Research.com in 2026. The highlighted bar marks where Ming Tang sits on this spectrum.

30 D-Index: 59 scientists 31 D-Index: 114 scientists 32 D-Index: 129 scientists 33 D-Index: 189 scientists 34 D-Index: 200 scientists 35 D-Index: 262 scientists 36 D-Index: 311 scientists 37 D-Index: 312 scientists 38 D-Index: 350 scientists 39 D-Index: 385 scientists 40 D-Index: 348 scientists 41 D-Index: 362 scientists 42 D-Index: 426 scientists 43 D-Index: 380 scientists 44 D-Index: 310 scientists 45 D-Index: 341 scientists 46 D-Index: 301 scientists 47 D-Index: 306 scientists 48 D-Index: 271 scientists 49 D-Index: 246 scientists 50 D-Index: 210 scientists 51 D-Index: 253 scientists 52 D-Index: 213 scientists 53 D-Index: 221 scientists 54 D-Index: 195 scientists 55 D-Index: 186 scientists 56 D-Index: 170 scientists 57 D-Index: 167 scientists 58 D-Index: 166 scientists 59 D-Index: 144 scientists 60 D-Index: 152 scientists 61 D-Index: 141 scientists 62 D-Index: 138 scientists 63 D-Index: 131 scientists 64 D-Index: 118 scientists 65 D-Index: 114 scientists 66 D-Index: 119 scientists 67 D-Index: 95 scientists 68 D-Index: 87 scientists 69 D-Index: 77 scientists 70 D-Index: 89 scientists 71 D-Index: 69 scientists 72 D-Index: 54 scientists 73 D-Index: 46 scientists 74 D-Index: 55 scientists 75 D-Index: 54 scientists 76 D-Index: 49 scientists 77 D-Index: 53 scientists 78 D-Index: 46 scientists 79 D-Index: 28 scientists 80 D-Index: 39 scientists 81 D-Index: 36 scientists 82 D-Index: 24 scientists 83 D-Index: 26 scientists 84 D-Index: 36 scientists 85 D-Index: 18 scientists 86 D-Index: 25 scientists 87 D-Index: 19 scientists 88 D-Index: 26 scientists 89 D-Index: 27 scientists 90 D-Index: 23 scientists 91 D-Index: 15 scientists 92 D-Index: 12 scientists 93 D-Index: 9 scientists 94 D-Index: 15 scientists 95 D-Index: 10 scientists 96 D-Index: 13 scientists 97 D-Index: 13 scientists 98 D-Index: 9 scientists 99 D-Index: 7 scientists 100 D-Index: 7 scientists 101 D-Index: 8 scientists 102 D-Index: 7 scientists 103 D-Index: 7 scientists 104 D-Index: 9 scientists 105 D-Index: 6 scientists 106 D-Index: 9 scientists 107+ D-Index: 99 scientists
30 D-Index 107+

This scientist: 41 D-Index — 31st percentile

31% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 107 D-Index or more.

Overview

Ming Tang is affiliated with East China Normal University in China and has contributed extensively to research in the fields of Physics and Astronomy as well as Medicine. Their work predominantly focuses on Statistical and Nonlinear Physics, with significant engagements in Computer Networks and Communications and Modeling and Simulation. Other areas of investigation include Mechanical Engineering and Experimental and Cognitive Psychology.

The scientist's main topics of study include:

  • Complex Network Analysis Techniques
  • Opinion Dynamics and Social Influence
  • COVID-19 Epidemiological Studies
  • Mental Health Research Topics
  • Nonlinear Dynamics and Pattern Formation
  • Hydraulic Fracturing and Reservoir Analysis
  • Opportunistic and Delay-Tolerant Networks

Recent publications by Ming Tang are as follows:

  • "Identifying super-spreaders in information-epidemic coevolving dynamics on multiplex networks," 2021, Knowledge-Based Systems
  • "Short-term forecasts to inform the response to the Covid-19 epidemic in the UK," 2020, bioRxiv (Cold Spring Harbor Laboratory)
  • "Non-Markovian recovery makes complex networks more resilient against large-scale failures," 2020, Nature Communications
  • "Experimental study on stress-dependent multiphase flow in ultra-low permeability sandstone during CO2 flooding based on LF-NMR," 2023, Energy
  • "Gompertz model in COVID-19 spreading simulation," 2021, Chaos Solitons & Fractals

Frequent co-authors collaborating with Ming Tang include:

  • Ying Liu
  • Shuguang Guan
  • Ying-Cheng Lai
  • Zonghua Liu
  • Yongshang Long

Ming Tang's publications appear regularly in several academic journals and venues. Most notably, these include:

  • Chaos Solitons & Fractals (11 publications)
  • arXiv (Cornell University) (7 publications)
  • Communications in Nonlinear Science and Numerical Simulation (5 publications)
  • Physical Review E (3 publications)
  • Journal of Physics Conference Series (3 publications)

Best Publications

  • Unification of theoretical approaches for epidemic spreading on complex networks.

    Wei Wang;Ming Tang;Ming Tang;H Eugene Stanley;Lidia A Braunstein;Lidia A Braunstein

  • Asymmetrically interacting spreading dynamics on complex layered networks

    Wei Wang;Ming Tang;Hui Yang;Younghae Do

  • Suppressing disease spreading by using information diffusion on multiplex networks.

    Wei Wang;Wei Wang;Quan-Hui Liu;Shi-Min Cai;Ming Tang

  • Improving the accuracy of the k-shell method by removing redundant links: From a perspective of spreading dynamics

    Ying Liu;Ming Tang;Tao Zhou;Younghae Do

  • Epidemic spreading with information-driven vaccination.

    Zhongyuan Ruan;Ming Tang;Zonghua Liu

  • Identify influential spreaders in complex networks, the role of neighborhood

    Ying Liu;Ying Liu;Ying Liu;Ming Tang;Tao Zhou;Younghae Do

  • Epidemic spreading on complex networks with general degree and weight distributions.

    Wei Wang;Ming Tang;Hai-Feng Zhang;Hui Gao

  • Dynamics of social contagions with memory of nonredundant information.

    Wei Wang;Ming Tang;Ming Tang;Hai Feng Zhang;Ying-Cheng Lai

  • Suppression of epidemic spreading in complex networks by local information based behavioral responses

    Hai Feng Zhang;Jia Rong Xie;Ming Tang;Ying Cheng Lai

  • Dynamics of social contagions with heterogeneous adoption thresholds: crossover phenomena in phase transition

    Wei Wang;Ming Tang;Panpan Shu;Zhen Wang

  • Self-adjusting Routing Schemes for Time-varying Traffic in Scale-free Networks

    Ming Tang;Zonghua Liu;Xiaoming Liang;P. M. Hui

  • Suppressing epidemic spreading in multiplex networks with social-support

    Xiaolong Chen;Ruijie Wang;Ming Tang;Ming Tang;Shimin Cai

  • Constructing ordinal partition transition networks from multivariate time series

    Jiayang Zhang;Jie Zhou;Ming Tang;Heng Guo

  • An adaptive routing strategy for packet delivery in complex networks

    Huan Zhang;Zonghua Liu;Ming Tang;Pak Ming Hui

  • Numerical identification of epidemic thresholds for susceptible-infected-recovered model on finite-size networks

    Panpan Shu;Wei Wang;Ming Tang;Younghae Do

  • Phase synchronization of inhibitory bursting neurons induced by distributed time delays in chemical coupling.

    Xiaoming Liang;Ming Tang;Mukeshwar Dhamala;Zonghua Liu

  • Effects of behavioral response and vaccination policy on epidemic spreading - an approach based on evolutionary-game dynamics

    Hai Feng Zhang;Zhi Xi Wu;Ming Tang;Ying-Cheng Lai

  • An Efficient Immunization Strategy for Community Networks

    Kai Gong;Ming Tang;Pak Ming Hui;Hai Feng Zhang

  • Efficient routing strategies in scale-free networks with limited bandwidth.

    Ming Tang;Tao Zhou;Tao Zhou

  • Preferential imitation can invalidate targeted subsidy policies on seasonal-influenza diseases

    Hai-Feng Zhang;Pan-Pan Shu;Zhen Wang;Ming Tang

Frequent Co-Authors

Ying-Cheng Lai
Ying-Cheng Lai Arizona State University
Hai-Feng Zhang
Hai-Feng Zhang Chinese Academy of Sciences
Tao Zhou
Tao Zhou University of Electronic Science and Technology of China
H. Eugene Stanley
H. Eugene Stanley Boston University
Jie Zhou
Jie Zhou Tsinghua University
Michael Small
Michael Small University of Western Australia
Zi-Ke Zhang
Zi-Ke Zhang Zhejiang University
Thilo Gross
Thilo Gross Carl von Ossietzky University of Oldenburg
Baowen Li
Baowen Li Southern University of Science and Technology
Stefano Boccaletti
Stefano Boccaletti Institute for Complex Systems

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