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Computer Science
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2026

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Best Scientists

D-Index
191
Citations
177329
World Ranking
398
National Ranking
264

Computer Science

D-Index
194
Citations
184846
World Ranking
7
National Ranking
4

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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