World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
86
Citations
36532
World Ranking
758
National Ranking
405

Jiliang Tang 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 Jiliang Tang 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: 321 publications — 77th percentile

77% of scientists in this discipline score the same or lower.

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

Jiliang Tang 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 Jiliang Tang 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: 86 D-Index — 95th percentile

95% of scientists in this discipline score the same or lower.

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

Overview

Jiliang Tang is a researcher affiliated with Michigan State University in the United States, specializing in computer science with a focus on artificial intelligence. Their scholarly work spans several subfields including information systems, computer vision and pattern recognition, molecular biology, and statistical and nonlinear physics.

Their primary research topics include advanced graph neural networks, topic modeling, recommender systems and techniques, natural language processing techniques, adversarial robustness in machine learning, anomaly detection techniques and applications, and complex network analysis techniques.

Jiliang Tang's recent publications include:

  • "Opening the Black Box: Interpretable Machine Learning for Geneticists" (2020) published in Trends in Genetics
  • "Recommender Systems in the Era of Large Language Models (LLMs)" (2024) published in IEEE Transactions on Knowledge and Data Engineering
  • "A Graph Neural Network Framework for Social Recommendations" (2020) published in IEEE Transactions on Knowledge and Data Engineering
  • "Trustworthy AI: A Computational Perspective" (2022) published in ACM Transactions on Intelligent Systems and Technology
  • "Adversarial Attacks and Defenses on Graphs" (2021) published in ACM SIGKDD Explorations Newsletter

They have frequently published in venues such as:

  • arXiv (Cornell University)
  • bioRxiv (Cold Spring Harbor Laboratory)
  • IEEE Transactions on Knowledge and Data Engineering
  • Proceedings of the AAAI Conference on Artificial Intelligence
  • Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

Jiliang Tang's frequent coauthors include Yao Ma, Wei Jin, Xiaorui Liu, Zitao Liu, and Wenqi Fan.

They have contributed to book publications, including a title published by Cambridge University Press called Deep Learning on Graphs (2021).

Best Publications

  • Fake News Detection on Social Media: A Data Mining Perspective

    Kai Shu;Amy Sliva;Suhang Wang;Jiliang Tang

  • Feature Selection: A Data Perspective

    Jundong Li;Kewei Cheng;Suhang Wang;Fred Morstatter

  • Graph Neural Networks for Social Recommendation

    Wenqi Fan;Yao Ma;Qing Li;Yuan He

  • Feature selection for classification: A review

    Jiliang Tang;Salem Alelyani;Huan Liu

  • Feature Selection

    Unknown

  • A Survey on Dialogue Systems: Recent Advances and New Frontiers

    Hongshen Chen;Xiaorui Liu;Dawei Yin;Jiliang Tang

  • Heterogeneous Network Embedding via Deep Architectures

    Shiyu Chang;Wei Han;Jiliang Tang;Guo-Jun Qi

  • Social recommendation: a review

    Jiliang Tang;Xia Hu;Huan Liu

  • Graph Structure Learning for Robust Graph Neural Networks

    Wei Jin;Yao Ma;Xiaorui Liu;Xianfeng Tang

  • Exploring temporal effects for location recommendation on location-based social networks

    Huiji Gao;Jiliang Tang;Xia Hu;Huan Liu

  • Adversarial Attacks and Defenses in Images, Graphs and Text: A Review

    Han Xu;Yao Ma;Hao-Chen Liu;Debayan Deb

  • Traffic Flow Prediction via Spatial Temporal Graph Neural Network

    Xiaoyang Wang;Yao Ma;Yiqi Wang;Wei Jin

  • Unsupervised sentiment analysis with emotional signals

    Xia Hu;Jiliang Tang;Huiji Gao;Huan Liu

  • Exploiting social relations for sentiment analysis in microblogging

    Xia Hu;Lei Tang;Jiliang Tang;Huan Liu

  • XGNN: Towards Model-Level Explanations of Graph Neural Networks

    Hao Yuan;Jiliang Tang;Xia Hu;Shuiwang Ji

  • Feature Selection for Clustering: A Review

    Salem Alelyani;Jiliang Tang;Huan Liu

  • Opening the Black Box: Interpretable Machine Learning for Geneticists.

    Christina B. Azodi;Christina B. Azodi;Jiliang Tang;Shin-Han Shiu

  • Deep reinforcement learning for page-wise recommendations

    Xiangyu Zhao;Long Xia;Liang Zhang;Zhuoye Ding

  • Recommendations with Negative Feedback via Pairwise Deep Reinforcement Learning

    Xiangyu Zhao;Liang Zhang;Zhuoye Ding;Long Xia

  • Content-aware point of interest recommendation on location-based social networks

    Huiji Gao;Jiliang Tang;Xia Hu;Huan Liu

  • Attributed Network Embedding for Learning in a Dynamic Environment

    Jundong Li;Harsh Dani;Xia Hu;Jiliang Tang

  • Exploiting homophily effect for trust prediction

    Jiliang Tang;Huiji Gao;Xia Hu;Huan Liu

  • Exploring Social-Historical Ties on Location-Based Social Networks

    Huiji Gao;Jiliang Tang;Huan Liu

  • mTrust: discerning multi-faceted trust in a connected world

    Jiliang Tang;Huiji Gao;Huan Liu

Frequent Co-Authors

Huan Liu
Huan Liu Arizona State University
Suhang Wang
Suhang Wang Pennsylvania State University
Dawei Yin
Dawei Yin Baidu (China)
Xia Hu
Xia Hu Rice University
Yi Chang
Yi Chang Jilin University
Charu C. Aggarwal
Charu C. Aggarwal IBM (United States)
Jundong Li
Jundong Li University of Virginia
Qing Li
Qing Li Hong Kong Polytechnic University
Jianping Wang
Jianping Wang City University of Hong Kong
Baoxin Li
Baoxin Li Shaanxi Normal University

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