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Computer Science

D-Index
194
Citations
184846
World Ranking
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National Ranking
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Philip S. Yu 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 Philip S. Yu 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: 250 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: 560 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: 424 scientists 242–251 publications: 408 scientists 252–261 publications: 378 scientists 262–271 publications: 300 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: 1,999 publications — 100th percentile

100% of scientists in this discipline score the same or lower.

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

Philip S. Yu 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 Philip S. Yu sits on this spectrum.

30–31 D-Index: 879 scientists 32–33 D-Index: 984 scientists 34–35 D-Index: 918 scientists 36–37 D-Index: 990 scientists 38–39 D-Index: 969 scientists 40–41 D-Index: 907 scientists 42–43 D-Index: 821 scientists 44–45 D-Index: 765 scientists 46–47 D-Index: 689 scientists 48–49 D-Index: 543 scientists 50–51 D-Index: 543 scientists 52–53 D-Index: 517 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: 194 D-Index — 100th percentile

100% of scientists in this discipline score the same or lower.

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

Research.com Recognitions

  • 2026 - Research.com Computer Science in United States Leader Award
  • 2025 - Research.com Best Scientists Award
  • 2025 - Research.com Computer Science in United States Leader Award
  • 2023 - Research.com Computer Science in United States Leader Award
  • 2022 - Research.com Computer Science in United States Leader Award
  • 1997 - ACM Fellow For contributions to the theory and practice of analytical performance modeling of database sytems.
  • 1993 - IEEE Fellow For contributions to the theory and practice of analytical performance modeling of database systems.

Overview

Philip S. Yu is affiliated with the University of Illinois at Chicago in the United States. Their research primarily spans the field of computer science, with a significant focus on artificial intelligence, information systems, computer vision and pattern recognition, statistical and nonlinear physics, and signal processing.

The scientist's recent publications include works across leading journals and conferences. Notable papers are:

  • "A Survey on Knowledge Graphs: Representation, Acquisition, and Applications" (2021) published in IEEE Transactions on Neural Networks and Learning Systems
  • "A Survey on Evaluation of Large Language Models" (2024) published in ACM Transactions on Intelligent Systems and Technology
  • "Generalizing to Unseen Domains: A Survey on Domain Generalization" (2022) published in IEEE Transactions on Knowledge and Data Engineering
  • "Deep Learning for Spatio-Temporal Data Mining: A Survey" (2020) published in IEEE Transactions on Knowledge and Data Engineering
  • "PredRNN: A Recurrent Neural Network for Spatiotemporal Predictive Learning" (2022) published in IEEE Transactions on Pattern Analysis and Machine Intelligence

Philip S. Yu has contributed to various main research topics, which include:

  • Advanced Graph Neural Networks
  • Topic Modeling
  • Recommender Systems and Techniques
  • Complex Network Analysis Techniques
  • Natural Language Processing Techniques
  • Data Mining Algorithms and Applications
  • Privacy-Preserving Technologies in Data

Frequent co-authors collaborating with Philip S. Yu encompass:

  • Hao Peng
  • Wensheng Gan
  • Lifang He
  • Jia Wu
  • Senzhang Wang

Publication venues where Philip S. Yu's works have frequently appeared include:

  • IEEE Transactions on Knowledge and Data Engineering
  • arXiv (Cornell University)
  • IEEE Transactions on Neural Networks and Learning Systems
  • Proceedings of the AAAI Conference on Artificial Intelligence
  • ACM Transactions on Knowledge Discovery from Data

Among their book publications are works released by Springer Science+Business Media and Springer International Publishing, including titles like "Data Science" (2020) and "Heterogeneous Graph Representation Learning and Applications" (2022).

Philip S. Yu has received recognition as an ACM Fellow in 1997 for contributions to the theory and practice of analytical performance modeling of database systems. They were also named IEEE Fellow in 1993 for related contributions.

Best Publications

  • A Comprehensive Survey on Graph Neural Networks

    Zonghan Wu;Shirui Pan;Fengwen Chen;Guodong Long

  • Top 10 algorithms in data mining

    Xindong Wu;Vipin Kumar;J. Ross Quinlan;Joydeep Ghosh

  • Data mining: an overview from a database perspective

    Ming-Syan Chen;Jiawei Han;P.S. Yu

  • A framework for clustering evolving data streams

    Charu C. Aggarwal;Jiawei Han;Jianyong Wang;Philip S. Yu

  • An effective hash-based algorithm for mining association rules

    Jong Soo Park;Ming-Syan Chen;Philip S. Yu

  • Heterogeneous Graph Attention Network

    Xiao Wang;Houye Ji;Chuan Shi;Bai Wang

  • A Survey on Knowledge Graphs: Representation, Acquisition and Applications

    Shaoxiong Ji;Shirui Pan;Erik Cambria;Pekka Marttinen

  • Privacy-preserving data publishing: A survey of recent developments

    Benjamin C. M. Fung;Ke Wang;Rui Chen;Philip S. Yu

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

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

  • Transfer Feature Learning with Joint Distribution Adaptation

    Mingsheng Long;Jianmin Wang;Guiguang Ding;Jiaguang Sun

  • Mining concept-drifting data streams using ensemble classifiers

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

  • A holistic lexicon-based approach to opinion mining

    Xiaowen Ding;Bing Liu;Philip S. Yu

  • Outlier detection for high dimensional data

    Charu C. Aggarwal;Philip S. Yu

  • Fast algorithms for projected clustering

    Charu C. Aggarwal;Joel L. Wolf;Philip S. Yu;Cecilia Procopiuc

  • A new method to measure the semantic similarity of GO terms

    James Z. Wang;Zhidian Du;Rapeeporn Payattakool;Philip S. Yu

  • A General Survey of Privacy-Preserving Data Mining Models and Algorithms

    Charu C. Aggarwal;Philip S. Yu

  • A Survey of Heterogeneous Information Network Analysis

    Chuan Shi;Yitong Li;Jiawei Zhang;Yizhou Sun

  • Heterogeneous Information Network Embedding for Recommendation

    Chuan Shi;Binbin Hu;Wayne Xin Zhao;Philip S. Yu

  • Dynamic load balancing on Web-server systems

    V. Cardellini;M. Colajanni;P.S. Yu

  • Joint Deep Modeling of Users and Items Using Reviews for Recommendation

    Lei Zheng;Vahid Noroozi;Philip S. Yu

  • Heterogeneous Graph Attention Network.

    Xiao Wang;Houye Ji;Chuan Shi;Bai Wang

Frequent Co-Authors

Charu C. Aggarwal
Charu C. Aggarwal IBM (United States)
Ming-Syan Chen
Ming-Syan Chen National Taiwan University
Kun-Lung Wu
Kun-Lung Wu IBM (United States)
Joel L. Wolf
Joel L. Wolf IBM (United States)
Wei Fan
Wei Fan Tencent (China)
Jiawei Han
Jiawei Han University of Illinois at Urbana-Champaign
Haixun Wang
Haixun Wang Instacart
Lifang He
Lifang He Lehigh University
Xiangnan Kong
Xiangnan Kong Worcester Polytechnic Institute
Chuan Shi
Chuan Shi Beijing University of Posts and Telecommunications

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