World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
64
Citations
15075
World Ranking
2620
National Ranking
355

Min Li 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 Min Li 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: 308 publications — 75th percentile

75% of scientists in this discipline score the same or lower.

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

Min Li 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 Min Li 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: 64 D-Index — 82nd percentile

82% of scientists in this discipline score the same or lower.

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

Overview

Min Li is affiliated with Central South University in China and has an extensive body of research primarily focused on engineering. Their academic contributions span several related fields, with a strong emphasis on electrical and electronic engineering as well as aerospace engineering and materials chemistry.

The main topics in Min Li's research include antenna design and analysis, microwave engineering and waveguides, full-duplex wireless communications, and advanced antenna and metasurface technologies. They have also worked on antenna design and optimization, supercapacitor materials and fabrication, and energy harvesting in wireless networks.

Min Li's research output includes numerous publications in prestigious venues. Frequent publication venues for their work are:

  • IEEE Transactions on Antennas and Propagation
  • IEEE Antennas and Wireless Propagation Letters
  • IEEE Transactions on Vehicular Technology
  • Sensors and Actuators B Chemical
  • Optics & Laser Technology

Some of Min Li's recent papers are:

  • "A General and Systematic Method to Design Neutralization Lines for Isolation Enhancement in MIMO Antenna Arrays" (2020), IEEE Transactions on Vehicular Technology
  • "Study on mechanical properties of alkali-resistant basalt fiber reinforced concrete" (2020), Construction and Building Materials

Other notable highly cited papers related to topics of interest but authored by frequent collaborators or coauthors include research on supercapacitors and antenna isolation enhancement.

Min Li collaborates regularly with researchers including Kwan L. Yeung, Lijun Jiang, Muhammad Yasir Jamal, Di Wu, and Ross Murch. These collaborations suggest active engagement in multidisciplinary projects within the electrical engineering and antenna research communities.

Best Publications

  • CytoNCA: a cytoscape plugin for centrality analysis and evaluation of protein interaction networks.

    Yu Tang;Min Li;Jianxin Wang;Yi Pan

  • A survey of MRI-based brain tumor segmentation methods

    Jin Liu;Min Li;Jianxin Wang;Fangxiang Wu

  • Drug repositioning based on comprehensive similarity measures and Bi-Random walk algorithm

    Huimin Luo;Jianxin Wang;Min Li;Junwei Luo

  • Identification of Essential Proteins Based on Edge Clustering Coefficient

    Jianxin Wang;Min Li;Huan Wang;Yi Pan

  • Applications of Deep Learning to MRI Images: A Survey

    Jin Liu;Yi Pan;Min Li;Ziyue Chen

  • Modifying the DPClus algorithm for identifying protein complexes based on new topological structures.

    Min Li;Jian-er Chen;Jian-er Chen;Jian-xin Wang;Bin Hu

  • Protein–protein interaction site prediction through combining local and global features with deep neural networks

    Min Zeng;Fuhao Zhang;Fang-Xiang Wu;Yaohang Li

  • A new essential protein discovery method based on the integration of protein-protein interaction and gene expression data

    Min Li;Min Li;Hanhui Zhang;Jian-xin Wang;Yi Pan;Yi Pan

  • Prediction of lncRNA-disease associations based on inductive matrix completion.

    Chengqian Lu;Mengyun Yang;Feng Luo;Fang-Xiang Wu

  • Computational drug repositioning using low-rank matrix approximation and randomized algorithms.

    Huimin Luo;Min Li;Shaokai Wang;Quan Liu

  • A Fast Hierarchical Clustering Algorithm for Functional Modules Discovery in Protein Interaction Networks

    Jianxin Wang;Min Li;Jianer Chen;Yi Pan

  • A local average connectivity-based method for identifying essential proteins from the network level

    Min Li;Jianxin Wang;Xiang Chen;Huan Wang

  • Classification of Alzheimer's Disease Using Whole Brain Hierarchical Network

    Jin Liu;Min Li;Wei Lan;Fang-Xiang Wu

  • LDAP: a web server for lncRNA-disease association prediction

    Wei Lan;Min Li;Kaijie Zhao;Jin Liu

  • A rapid SERS method for label-free bacteria detection using polyethylenimine-modified Au-coated magnetic microspheres and Au@Ag nanoparticles.

    Chongwen Wang;Junfeng Wang;Min Li;Xinyan Qu

  • SERS-Active MIL-100(Fe) Sensory Array for Ultrasensitive and Multiplex Detection of VOCs.

    Jing-Hao Fu;Jing-Hao Fu;Zhen Zhong;Dan Xie;Yan-Jun Guo

  • Biomedical data and computational models for drug repositioning: a comprehensive review.

    Huimin Luo;Min Li;Mengyun Yang;Fang-Xiang Wu

  • DeepDTAF: a deep learning method to predict protein-ligand binding affinity.

    Kaili Wang;Renyi Zhou;Yaohang Li;Min Li

  • Recent advances in clustering methods for protein interaction networks.

    Jianxin Wang;Jianxin Wang;Min Li;Youping Deng;Yi Pan

  • Construction and application of dynamic protein interaction network based on time course gene expression data.

    Jianxin Wang;Xiaoqing Peng;Min Li;Yi Pan;Yi Pan

  • ClusterViz: a cytoscape APP for cluster analysis of biological network

    Jianxin Wang;Jiancheng Zhong;Gang Chen;Min Li

  • Towards the identification of protein complexes and functional modules by integrating PPI network and gene expression data.

    Min Li;Xuehong Wu;Jianxin Wang;Yi Pan;Yi Pan

Frequent Co-Authors

Jianxin Wang
Jianxin Wang Central South University
Fang-Xiang Wu
Fang-Xiang Wu University of Saskatchewan
Yaohang Li
Yaohang Li Old Dominion University
Jianer Chen
Jianer Chen Texas A&M University
Shelley D. Minteer
Shelley D. Minteer University of Utah
Haixiang Gao
Haixiang Gao China Agricultural University
Jiaheng Zhang
Jiaheng Zhang Harbin Institute of Technology
Lukasz Kurgan
Lukasz Kurgan Virginia Commonwealth University
Lusheng Wang
Lusheng Wang City University of Hong Kong
James F. Martin
James F. Martin Baylor College of Medicine

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