World's Best Scientists 2026 revealed!
Haixun Wang

Haixun Wang

D-Index & Metrics

Computer Science

D-Index
78
Citations
22555
World Ranking
1212
National Ranking
644

Haixun Wang 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 Haixun Wang 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: 301 publications — 74th percentile

74% of scientists in this discipline score the same or lower.

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

Haixun Wang 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 Haixun Wang 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: 78 D-Index — 92nd percentile

92% of scientists in this discipline score the same or lower.

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

Overview

Haixun Wang is a researcher affiliated with Instacart in the United States, with a focus in computer science. Their scholarly work spans several subfields including artificial intelligence, information systems, pulmonary and respiratory medicine, sociology and political science, and computer vision and pattern recognition.

Their research topics cover a range of areas including:

  • Web Data Mining and Analysis
  • Semantic Web and Ontologies
  • Natural Language Processing Techniques
  • Recommender Systems and Techniques
  • Adversarial Robustness in Machine Learning
  • Topic Modeling
  • Occupational and Environmental Lung Diseases

Haixun Wang has contributed to multiple publication venues, with frequent appearances in:

  • arXiv (Cornell University)
  • Proceedings of the 31st ACM International Conference on Information & Knowledge Management
  • ACM SIGIR Forum
  • Proceedings of the VLDB Endowment
  • 2022 IEEE International Conference on Big Data (Big Data)

Their recent papers include:

  • "Adversarial Robustness through Bias Variance Decomposition," 2022, Proceedings of the 31st ACM International Conference on Information & Knowledge Management
  • "From Intrinsic to Counterfactual: On the Explainability of Contextualized Recommender Systems," 2021, arXiv (Cornell University)
  • "Rethinking E-Commerce Search," 2023, ACM SIGIR Forum
  • "Will LLMs Reshape, Supercharge, or Kill Data Science? (VLDB 2023 Panel)," 2023, Proceedings of the VLDB Endowment
  • "Tensor-based Complementary Product Recommendation," 2021, 2021 IEEE International Conference on Big Data (Big Data)

Frequent collaborators in Haixun Wang's research include Taesik Na, Yao Zhou, Jingrui He, Jun Wu, and Shupeng Liu.

Best Publications

  • Mining concept-drifting data streams using ensemble classifiers

    Haixun Wang;Wei Fan;Philip S. Yu;Jiawei Han

  • Probase: a probabilistic taxonomy for text understanding

    Wentao Wu;Hongsong Li;Haixun Wang;Kenny Q. Zhu

  • BLINKS: ranked keyword searches on graphs

    Hao He;Haixun Wang;Jun Yang;Philip S. Yu

  • Clustering by pattern similarity in large data sets

    Haixun Wang;Wei Wang;Jiong Yang;Philip S. Yu

  • Trinity: a distributed graph engine on a memory cloud

    Bin Shao;Haixun Wang;Yatao Li

  • Managing and Mining Graph Data

    Charu C. Aggarwal;Haixun Wang

  • ViST: a dynamic index method for querying XML data by tree structures

    Haixun Wang;Sanghyun Park;Wei Fan;Philip S. Yu

  • Landmarks: a new model for similarity-based pattern querying in time series databases

    C.-S. Perng;H. Wang;S.R. Zhang;D.S. Parker

  • /spl delta/-clusters: capturing subspace correlation in a large data set

    Jiong Yang;Wei Wang;Haixun Wang;P. Yu

  • Moment: maintaining closed frequent itemsets over a stream sliding window

    Yun Chi;Haixun Wang;P.S. Yu;R.R. Muntz

  • Enhanced biclustering on expression data

    Jiong Yang;Haixun Wang;Wei Wang;P. Yu

  • A distributed graph engine for web scale RDF data

    Kai Zeng;Jiacheng Yang;Haixun Wang;Bin Shao

  • Efficient subgraph matching on billion node graphs

    Zhao Sun;Hongzhi Wang;Haixun Wang;Bin Shao

  • Dual Labeling: Answering Graph Reachability Queries in Constant Time

    Haixun Wang;Hao He;Jun Yang;P.S. Yu

  • Local search of communities in large graphs

    Wanyun Cui;Yanghua Xiao;Haixun Wang;Wei Wang

  • Natural language question answering over RDF: a graph data driven approach

    Lei Zou;Ruizhe Huang;Haixun Wang;Jeffrey Xu Yu

  • Integrity auditing of outsourced data

    Min Xie;Haixun Wang;Jian Yin;Xiaofeng Meng

  • KBQA: learning question answering over QA corpora and knowledge bases

    Wanyun Cui;Yanghua Xiao;Haixun Wang;Yangqiu Song

  • Answering Natural Language Questions by Subgraph Matching over Knowledge Graphs

    Sen Hu;Lei Zou;Jeffrey Xu Yu;Haixun Wang

  • Short text conceptualization using a probabilistic knowledgebase

    Yangqiu Song;Haixun Wang;Zhongyuan Wang;Hongsong Li

  • Efficiently answering reachability queries on very large directed graphs

    Ruoming Jin;Yang Xiang;Ning Ruan;Haixun Wang

Frequent Co-Authors

Philip S. Yu
Philip S. Yu University of Illinois at Chicago
Wei Fan
Wei Fan Tencent (China)
Carlo Zaniolo
Carlo Zaniolo University of California, Los Angeles
Min Wang
Min Wang Google (United States)
Jian Pei
Jian Pei Duke University
Jeffrey Xu Yu
Jeffrey Xu Yu Chinese University of Hong Kong
Xiaofeng Meng
Xiaofeng Meng Renmin University of China
Xuemin Lin
Xuemin Lin Shanghai Jiao Tong University
Ruoming Jin
Ruoming Jin Kent State University
Charu C. Aggarwal
Charu C. Aggarwal IBM (United States)

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