Philip S. Yu

135.3k total citations · 49 hit papers
1.6k papers, 75.6k citations indexed

About

Philip S. Yu is a scholar working on Artificial Intelligence, Information Systems and Computer Networks and Communications. According to data from OpenAlex, Philip S. Yu has authored 1.6k papers receiving a total of 75.6k indexed citations (citations by other indexed papers that have themselves been cited), including 918 papers in Artificial Intelligence, 495 papers in Information Systems and 441 papers in Computer Networks and Communications. Recurrent topics in Philip S. Yu's work include Advanced Graph Neural Networks (279 papers), Complex Network Analysis Techniques (251 papers) and Data Management and Algorithms (223 papers). Philip S. Yu is often cited by papers focused on Advanced Graph Neural Networks (279 papers), Complex Network Analysis Techniques (251 papers) and Data Management and Algorithms (223 papers). Philip S. Yu collaborates with scholars based in United States, China and Australia. Philip S. Yu's co-authors include Charų C. Aggarwal, Ming-Syan Chen⋆, Jiawei Han, Jianmin Wang, Mingsheng Long, Haixun Wang, Jong Soo Park, Joel L. Wolf, Xifeng Yan and Bing Liu and has published in prestigious journals such as Nucleic Acids Research, SHILAP Revista de lepidopterología and Bioinformatics.

In The Last Decade

Philip S. Yu

1.5k papers receiving 71.6k citations

Hit Papers

Top 10 algorithms in data... 1995 2026 2005 2015 2007 2021 2013 1996 2011 1000 2.0k 3.0k
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. Top 10 algorithms in data mining
    2007 3.9k citations Philip S. Yu et al. profile →
  2. A Survey on Knowledge Graphs: Representation, Acquisition, and Applications
    2021 1.5k citations Philip S. Yu et al. IEEE Transactions on Neural Networks and Learning Systems profile →
  3. Transfer Feature Learning with Joint Distribution Adaptation
    2013 1.4k citations Mingsheng Long, Jianmin Wang et al. profile →
  4. Data mining: an overview from a database perspective
    1996 1.4k citations Philip S. Yu et al. profile →
  5. PathSim
    2011 1.2k citations Philip S. Yu et al. profile →
  6. Privacy-preserving data publishing
    2010 1.0k citations Philip S. Yu et al. profile →
  7. A Survey on Evaluation of Large Language Models
    2024 1.0k citations Philip S. Yu et al. profile →
  8. A new method to measure the semantic similarity of GO terms
    2007 915 citations Philip S. Yu et al. profile →
  9. An effective hash-based algorithm for mining association rules
    1995 903 citations Philip S. Yu et al. profile →
  10. A holistic lexicon-based approach to opinion mining
    2008 881 citations Philip S. Yu et al. profile →
  11. Heterogeneous Information Network Embedding for Recommendation
    2018 792 citations Philip S. Yu et al. profile →
  12. Mining concept-drifting data streams using ensemble classifiers
    2003 783 citations Philip S. Yu et al. profile →
  13. A survey of heterogeneous information network analysis
    2016 687 citations Philip S. Yu et al. profile →
  14. Outlier detection for high dimensional data
    2001 667 citations Philip S. Yu et al. profile →
  15. Fast algorithms for projected clustering
    1999 612 citations Philip S. Yu et al. profile →
  16. Generalizing to Unseen Domains: A Survey on Domain Generalization
    2022 596 citations Philip S. Yu et al. profile →
  17. Transfer Joint Matching for Unsupervised Domain Adaptation
    2014 518 citations Mingsheng Long, Jianmin Wang et al. profile →
  18. Adaptation Regularization: A General Framework for Transfer Learning
    2013 482 citations Mingsheng Long, Jianmin Wang et al. profile →
  19. Deep Learning for Spatio-Temporal Data Mining: A Survey
    2020 460 citations Philip S. Yu et al. profile →
  20. Visual Domain Adaptation with Manifold Embedded Distribution Alignment
    2018 450 citations Philip S. Yu et al. profile →
  21. HashNet: Deep Learning to Hash by Continuation
    2017 446 citations Mingsheng Long, Jianmin Wang et al. profile →
  22. Leveraging Meta-path based Context for Top- N Recommendation with A Neural Co-Attention Model
    2018 425 citations Philip S. Yu et al. profile →
  23. PredRNN: recurrent neural networks for predictive learning using spatiotemporal LSTMs
    2017 417 citations Yunbo Wang, Mingsheng Long et al. profile →
  24. Truth Discovery with Multiple Conflicting Information Providers on the Web
    2008 413 citations Philip S. Yu et al. profile →
  25. Efficient Algorithms for Mining High Utility Itemsets from Transactional Databases
    2012 380 citations Philip S. Yu et al. profile →
  26. Direction-of-Arrival Estimation Based on Deep Neural Networks With Robustness to Array Imperfections
    2018 377 citations Chenwei Zhang, Philip S. Yu et al. profile →
  27. GraphScope
    2007 349 citations Philip S. Yu et al. profile →
  28. BLINKS
    2007 342 citations Philip S. Yu et al. profile →
  29. Graph Self-Supervised Learning: A Survey
    2022 330 citations Philip S. Yu et al. profile →
  30. PredRNN: A Recurrent Neural Network for Spatiotemporal Predictive Learning
    2022 317 citations Yunbo Wang, Jianmin Wang et al. profile →
  31. UP-Growth
    2010 300 citations Philip S. Yu et al. profile →
  32. Memory in Memory: A Predictive Neural Network for Learning Higher-Order Non-Stationarity From Spatiotemporal Dynamics
    2019 291 citations Yunbo Wang, Mingsheng Long et al. profile →
  33. Privacy and Robustness in Federated Learning: Attacks and Defenses
    2022 264 citations Lichao Sun, Philip S. Yu et al. IEEE Transactions on Neural Networks and Learning Systems profile →
  34. Review spam detection via temporal pattern discovery
    2012 252 citations Sihong Xie, Philip S. Yu et al. profile →
  35. Membership Inference Attacks on Machine Learning: A Survey
    2022 251 citations Lichao Sun, Philip S. Yu et al. profile →
  36. Improving automatic source code summarization via deep reinforcement learning
    2018 249 citations Yao Wan, Philip S. Yu et al. profile →
  37. Spatial temporal incidence dynamic graph neural networks for traffic flow forecasting
    2020 243 citations Hao Peng, Philip S. Yu et al. profile →
  38. A Survey on Heterogeneous Graph Embedding: Methods, Techniques, Applications and Sources
    2022 225 citations Philip S. Yu et al. IEEE Transactions on Big Data profile →
  39. A Survey on Text Classification: From Traditional to Deep Learning
    2022 218 citations Hao Peng, Lichao Sun et al. profile →
  40. A comprehensive survey on pretrained foundation models: a history from BERT to ChatGPT
    2024 178 citations Lifang He, Philip S. Yu et al. profile →
  41. An Integrated Cluster Detection, Optimization, and Interpretation Approach for Financial Data
    2021 164 citations Philip S. Yu et al. profile →
  42. Self-Supervised Discriminative Feature Learning for Deep Multi-View Clustering
    2022 154 citations Jie Xu, Yazhou Ren et al. profile →
  43. User Preference-aware Fake News Detection
    2021 152 citations Philip S. Yu, Lichao Sun et al. profile →
  44. Federated Social Recommendation with Graph Neural Network
    2022 116 citations Zhiwei Liu, Liangwei Yang et al. profile →
  45. Decentralized Federated Learning: A Survey and Perspective
    2024 88 citations Lichao Sun, Philip S. Yu et al. profile →
  46. HAQJSK: Hierarchical-Aligned Quantum Jensen-Shannon Kernels for Graph Classification
    2024 65 citations Philip S. Yu et al. profile →
  47. Deep Clustering: A Comprehensive Survey
    2024 64 citations Yazhou Ren, Jie Xu et al. IEEE Transactions on Neural Networks and Learning Systems profile →
  48. Large language models in law: A survey
    2024 35 citations Wensheng Gan, Jiayang Wu et al. SHILAP Revista de lepidopterología profile →
  49. A survey of multilingual large language models
    2025 17 citations Philip S. Yu et al. profile →

Author Peers

Peers are selected by citation overlap in the author's most active subfields. citations · hero ref

Author Last Decade Papers Cites
Philip S. Yu 43.0k 22.9k 14.8k 12.5k 12.4k 1.6k 75.6k
Jiawei Han 34.7k 0.8× 27.1k 1.2× 10.1k 0.7× 10.5k 0.8× 14.2k 1.1× 742 66.1k
Michael I. Jordan 48.1k 1.1× 11.6k 0.5× 8.4k 0.6× 19.3k 1.5× 8.5k 0.7× 556 96.0k
Christos Faloutsos 16.1k 0.4× 8.2k 0.4× 12.8k 0.9× 10.9k 0.9× 10.9k 0.9× 615 44.7k
Jürgen Schmidhuber 43.5k 1.0× 5.8k 0.3× 5.4k 0.4× 23.5k 1.9× 11.4k 0.9× 240 104.9k
Qiang Yang 32.1k 0.7× 11.1k 0.5× 6.3k 0.4× 13.7k 1.1× 4.7k 0.4× 860 62.9k
George Karypis 12.7k 0.3× 14.3k 0.6× 7.3k 0.5× 7.0k 0.6× 3.9k 0.3× 342 35.5k
Jure Leskovec 20.8k 0.5× 9.2k 0.4× 7.6k 0.5× 5.8k 0.5× 2.5k 0.2× 275 49.6k
Jian Pei 16.1k 0.4× 15.8k 0.7× 5.9k 0.4× 2.8k 0.2× 8.4k 0.7× 466 30.4k
Andrew Y. Ng 39.6k 0.9× 8.4k 0.4× 3.0k 0.2× 21.0k 1.7× 5.4k 0.4× 208 68.8k
Ian H. Witten 26.2k 0.6× 12.4k 0.5× 6.3k 0.4× 8.8k 0.7× 6.7k 0.5× 327 52.3k

Countries citing papers authored by Philip S. Yu

Since Specialization
Citations

This map shows the geographic impact of Philip S. Yu's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Philip S. Yu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Philip S. Yu more than expected).

Fields of papers citing papers by Philip S. Yu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Philip S. Yu. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Philip S. Yu. The network helps show where Philip S. Yu may publish in the future.

Co-authorship network of co-authors of Philip S. Yu

This figure shows the co-authorship network connecting the top 25 collaborators of Philip S. Yu. A scholar is included among the top collaborators of Philip S. Yu based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Philip S. Yu. Philip S. Yu is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Yu, Philip S., et al.. (2026). Watermarking techniques for large language models: a survey. Artificial Intelligence Review. 59(2).
2.
Yu, Philip S., Guoxin Chen, & Jingjing Wang. (2025). Table-Critic: A Multi-Agent Framework for Collaborative Criticism and Refinement in Table Reasoning. 17432–17451.
3.
Zhu, Tianqing, et al.. (2025). Knowledge Distillation in Federated Learning: A Survey on Long Lasting Challenges and New Solutions. International Journal of Intelligent Systems. 2025(1). 1 indexed citations
4.
5.
6.
Wang, Shen, Jianhe Xie, Xuming Hu, et al.. (2025). LLM Agents for Education: Advances and Applications. 13782–13810. 4 indexed citations
7.
8.
Gan, Wensheng, et al.. (2024). Large language models in law: A survey. SHILAP Revista de lepidopterología. 5. 181–196. 35 indexed citations breakdown →
9.
Chen, Hechang, et al.. (2024). A Contrastive-Enhanced Ensemble Framework for Efficient Multi-Agent Reinforcement Learning. Expert Systems with Applications. 245. 123158–123158. 6 indexed citations
10.
Zhu, Tianqing, et al.. (2024). Update Selective Parameters: Federated Machine Unlearning Based on Model Explanation. IEEE Transactions on Big Data. 11(2). 524–539. 4 indexed citations
11.
Ren, Yazhou, Jie Xu, Guofeng Li, et al.. (2024). Deep Clustering: A Comprehensive Survey. IEEE Transactions on Neural Networks and Learning Systems. 36(4). 5858–5878. 64 indexed citations breakdown →
12.
Sun, Li, et al.. (2024). Motif-Aware Riemannian Graph Neural Network with Generative-Contrastive Learning. Proceedings of the AAAI Conference on Artificial Intelligence. 38(8). 9044–9052. 4 indexed citations
13.
Nguyen, Hoang, et al.. (2023). Slot Induction via Pre-trained Language Model Probing and Multi-level Contrastive Learning. 470–481. 1 indexed citations
14.
Liu, Chuanren, et al.. (2022). Recent advances in domain-driven data mining. International Journal of Data Science and Analytics. 15(1). 1–7. 3 indexed citations
15.
Wang, Shen, Liangwei Yang, Jibing Gong, et al.. (2022). MetaKRec: Collaborative Meta-Knowledge Enhanced Recommender System. 2022 IEEE International Conference on Big Data (Big Data). 665–674. 3 indexed citations
16.
Zhang, Jiawei, Bowen Dong, & Philip S. Yu. (2020). FakeDetector: Effective Fake News Detection with Deep Diffusive Neural Network. 1826–1829. 145 indexed citations
17.
Hu, Xuming, et al.. (2020). SelfORE: Self-supervised Relational Feature Learning for Open Relation Extraction. 3673–3682. 48 indexed citations
18.
Wang, Yunbo, Mingsheng Long, Jianmin Wang, & Philip S. Yu. (2017). Spatiotemporal Pyramid Network for Video Action Recognition. 2097–2106. 190 indexed citations
19.
Wang, Guan, Sihong Xie, Bing Liu, & Philip S. Yu. (2011). Review Graph Based Online Store Review Spammer Detection. 1242–1247. 243 indexed citations
20.
Vlachos, Michail, Claudio Lucchese, Deepak Rajan, & Philip S. Yu. (2008). Ownership protection of shape datasets with geodesic distance preservation. ISTI Open Portal. 276–286. 3 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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