World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
50
Citations
18646
World Ranking
5483
National Ranking
733

Kang Liu 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 Kang Liu 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: 218 publications — 53rd percentile

53% of scientists in this discipline score the same or lower.

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

Kang Liu 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 Kang Liu 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: 50 D-Index — 62nd percentile

62% of scientists in this discipline score the same or lower.

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

Overview

Kang Liu is affiliated with the Chinese Academy of Sciences in China. Their primary field of study is Computer Science, with a significant focus on Artificial Intelligence. Other areas of research include Computer Vision and Pattern Recognition, Information Systems, Paleontology, and Computer Networks and Communications.

The scientist's research spans several key topics, including:

  • Topic Modeling
  • Natural Language Processing Techniques
  • Advanced Graph Neural Networks
  • Text and Document Classification Technologies
  • Advanced Text Analysis Techniques
  • Multimodal Machine Learning Applications
  • Explainable Artificial Intelligence (XAI)

Kang Liu has contributed to numerous publication venues, with the most frequent ones being:

  • arXiv (Cornell University)
  • ACM Transactions on Asian and Low-Resource Language Information Processing
  • IEEE Transactions on Audio Speech and Language Processing
  • Journal of Computer Science and Technology
  • SSRN Electronic Journal

Among the recent papers associated with their work are:

  • Extracting Events and Their Relations from Texts: A Survey on Recent Research Progress and Challenges, 2020, AI Open
  • A survey on neural relation extraction, 2020, Science China Technological Sciences

Kang Liu has collaborated frequently with several co-authors, including:

  • Jun Zhao
  • Yubo Chen
  • Shizhu He
  • Yuanzhe Zhang

In addition to journal articles, Kang Liu has published books, including one titled Knowledge Graph and Semantic Computing: Knowledge Graph Empowers the Digital Economy, published by Springer Science+Business Media in 2022.

Best Publications

  • Recurrent convolutional neural networks for text classification

    Siwei Lai;Liheng Xu;Kang Liu;Jun Zhao

  • Knowledge Graph Embedding via Dynamic Mapping Matrix

    Guoliang Ji;Shizhu He;Liheng Xu;Kang Liu

  • Relation Classification via Convolutional Deep Neural Network

    Daojian Zeng;Kang Liu;Siwei Lai;Guangyou Zhou

  • A Brief Overview of ChatGPT: The History, Status Quo and Potential Future Development

    Unknown

  • Distant Supervision for Relation Extraction via Piecewise Convolutional Neural Networks

    Daojian Zeng;Kang Liu;Yubo Chen;Jun Zhao

  • Event Extraction via Dynamic Multi-Pooling Convolutional Neural Networks

    Yubo Chen;Liheng Xu;Kang Liu;Daojian Zeng

  • Extracting Relational Facts by an End-to-End Neural Model with Copy Mechanism

    Xiangrong Zeng;Daojian Zeng;Shizhu He;Kang Liu

  • Knowledge graph completion with adaptive sparse transfer matrix

    Guoliang Ji;Kang Liu;Shizhu He;Jun Zhao

  • How to Generate a Good Word Embedding

    Siwei Lai;Kang Liu;Shizhu He;Jun Zhao

  • Learning to Represent Knowledge Graphs with Gaussian Embedding

    Shizhu He;Kang Liu;Guoliang Ji;Jun Zhao

  • Distant Supervision for Relation Extraction with Sentence-level Attention and Entity Descriptions

    Guoliang Ji;Kang Liu;Shizhu He;Jun Zhao

  • An End-to-End Model for Question Answering over Knowledge Base with Cross-Attention Combining Global Knowledge

    Yanchao Hao;Yuanzhe Zhang;Kang Liu;Shizhu He

  • Event Extraction as Machine Reading Comprehension.

    Jian Liu;Yubo Chen;Kang Liu;Wei Bi

  • Exploiting Argument Information to Improve Event Detection via Supervised Attention Mechanisms

    Shulin Liu;Yubo Chen;Kang Liu;Jun Zhao

  • Inner Attention based Recurrent Neural Networks for Answer Selection

    Bingning Wang;Kang Liu;Jun Zhao

  • Adversarial Transfer Learning for Chinese Named Entity Recognition with Self-Attention Mechanism

    Pengfei Cao;Yubo Chen;Kang Liu;Jun Zhao

  • Phrase-Based Translation Model for Question Retrieval in Community Question Answer Archives

    Guangyou Zhou;Li Cai;Jun Zhao;Kang Liu

  • Opinion Target Extraction Using Word-Based Translation Model

    Kang Liu;Liheng Xu;Jun Zhao

  • Automatically Labeled Data Generation for Large Scale Event Extraction

    Yubo Chen;Shulin Liu;Xiang Zhang;Kang Liu

  • DCFEE: A Document-level Chinese Financial Event Extraction System based on Automatically Labeled Training Data

    Hang Yang;Yubo Chen;Kang Liu;Yang Xiao

  • Adding Redundant Features for CRFs-based Sentence Sentiment Classification

    Jun Zhao;Kang Liu;Gen Wang

Frequent Co-Authors

Jun Zhao
Jun Zhao Chinese Academy of Sciences
Yang Liu
Yang Liu Tsinghua University
Qi Zhang
Qi Zhang Fudan University
Ruobing Xie
Ruobing Xie Tencent (China)
Hua Wu
Hua Wu Baidu (China)
Xiapu Luo
Xiapu Luo Hong Kong Polytechnic University
Gerard de Melo
Gerard de Melo Hasso Plattner Institute
Yansong Feng
Yansong Feng Peking University
Yulan He
Yulan He King's College London
Shiming Xiang
Shiming Xiang Chinese Academy of Sciences

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