World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
74
Citations
27321
World Ranking
1468
National Ranking
764

Xifeng Yan 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 Xifeng Yan 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: 253 publications — 63rd percentile

63% of scientists in this discipline score the same or lower.

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

Xifeng Yan 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 Xifeng Yan 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: 74 D-Index — 90th percentile

90% of scientists in this discipline score the same or lower.

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

Overview

Xifeng Yan is a researcher affiliated with the University of California, Santa Barbara in the United States. Their work primarily covers the field of Computer Science, with a significant focus on Artificial Intelligence, as well as notable contributions to Computer Vision and Pattern Recognition, Materials Chemistry, Information Systems, and Modeling and Simulation.

The main research topics explored by Xifeng Yan include:

  • Topic Modeling
  • Natural Language Processing Techniques
  • Domain Adaptation and Few-Shot Learning
  • Multimodal Machine Learning Applications
  • Advanced Graph Neural Networks
  • COVID-19 Epidemiological Studies
  • Titanium Alloys Microstructure and Properties

Among the recent papers associated with this researcher are:

  • Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States, 2022, Proceedings of the National Academy of Sciences
  • The United States COVID-19 Forecast Hub dataset, 2022, Scientific Data
  • Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the US, 2021, bioRxiv (Cold Spring Harbor Laboratory)
  • CoCo: Controllable Counterfactuals for Evaluating Dialogue State Trackers, 2020, arXiv (Cornell University)
  • Inductive Relation Prediction by BERT, 2022, Proceedings of the AAAI Conference on Artificial Intelligence

Frequent co-authors collaborating with Xifeng Yan include:

  • Jiajun Bu
  • Weizhi Wang
  • Xiaoyong Jin
  • Zekun Li
  • Yu-Xiang Wang

The majority of Xifeng Yan's publications appear in the following venues:

  • arXiv (Cornell University)
  • Proceedings of the AAAI Conference on Artificial Intelligence
  • Journal of Alloys and Compounds
  • Zenodo (CERN European Organization for Nuclear Research)
  • SSRN Electronic Journal

Best Publications

  • gSpan: graph-based substructure pattern mining

    Xifeng Yan;Jiawei Han

  • PathSim: meta path-based top-K similarity search in heterogeneous information networks

    Yizhou Sun;Jiawei Han;Xifeng Yan;Philip S. Yu

  • Frequent pattern mining: current status and future directions

    Jiawei Han;Hong Cheng;Dong Xin;Xifeng Yan

  • Enhancing the Locality and Breaking the Memory Bottleneck of Transformer on Time Series Forecasting

    Shiyang Li;Xiaoyong Jin;Yao Xuan;Xiyou Zhou

  • CloSpan: Mining Closed Sequential Patterns in Large Datasets

    Unknown

  • Graph indexing: a frequent structure-based approach

    Xifeng Yan;Philip S. Yu;Jiawei Han

  • CloseGraph: mining closed frequent graph patterns

    Xifeng Yan;Jiawei Han

  • Mining Frequent Patterns in Data Streams at Multiple Time Granularities

    Chris Giannella;Jiawei Han;Xifeng Yan;Philip S. Yu

  • SOBER: statistical model-based bug localization

    Chao Liu;Xifeng Yan;Long Fei;Jiawei Han

  • Discriminative Frequent Pattern Analysis for Effective Classification

    Hong Cheng;Xifeng Yan;Jiawei Han;Chih-Wei Hsu

  • Mining coherent dense subgraphs across massive biological networks for functional discovery

    Haiyan Hu;Xifeng Yan;Yu Huang;Jiawei Han

  • Substructure similarity search in graph databases

    Xifeng Yan;Philip S. Yu;Jiawei Han

  • PathSelClus: Integrating Meta-Path Selection with User-Guided Object Clustering in Heterogeneous Information Networks

    Yizhou Sun;Brandon Norick;Jiawei Han;Xifeng Yan

  • Statistical Debugging: A Hypothesis Testing-Based Approach

    Chao Liu;Long Fei;Xifeng Yan;Jiawei Han

  • Mining significant graph patterns by leap search

    Xifeng Yan;Hong Cheng;Jiawei Han;Philip S. Yu

  • Workload characterization and prediction in the cloud: A multiple time series approach

    Arijit Khan;Xifeng Yan;Shu Tao;Nikos Anerousis

  • TSP: Mining top-k closed sequential patterns

    Petre Tzvetkov;Xifeng Yan;Jiawei Han

  • IncSpan: incremental mining of sequential patterns in large database

    Hong Cheng;Xifeng Yan;Jiawei Han

  • Mining compressed frequent-pattern sets

    Dong Xin;Jiawei Han;Xifeng Yan;Hong Cheng

  • Synthesizing Near-Optimal Malware Specifications from Suspicious Behaviors

    Matt Fredrikson;Somesh Jha;Mihai Christodorescu;Reiner Sailer

  • Direct Discriminative Pattern Mining for Effective Classification

    Hong Cheng;Xifeng Yan;Jiawei Han;P.S. Yu

Frequent Co-Authors

Jiawei Han
Jiawei Han University of Illinois at Urbana-Champaign
Philip S. Yu
Philip S. Yu University of Illinois at Chicago
Yu Su
Yu Su The Ohio State University
Feida Zhu
Feida Zhu Singapore Management University
Mudhakar Srivatsa
Mudhakar Srivatsa IBM (United States)
Ambuj K. Singh
Ambuj K. Singh University of California, Santa Barbara
William Yang Wang
William Yang Wang University of California, Santa Barbara
Yizhou Sun
Yizhou Sun University of California, Los Angeles
Amr El Abbadi
Amr El Abbadi University of California, Santa Barbara
Karsten M. Borgwardt
Karsten M. Borgwardt Max Planck Institute of Biochemistry

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