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 mining 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. 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. IEEE Transactions on Knowledge and Data Engineering 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 Binbin Hu, Wayne Xin Zhao et al. IEEE Transactions on Knowledge and Data Engineering 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. IEEE Transactions on Knowledge and Data Engineering 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. IEEE Transactions on Knowledge and Data Engineering 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. IEEE Transactions on Knowledge and Data Engineering profile →
  19. Deep Learning for Spatio-Temporal Data Mining: A Survey
    2020 460 citations Senzhang Wang, Philip S. Yu et al. IEEE Transactions on Knowledge and Data Engineering 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 Binbin Hu, Wayne Xin Zhao 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. IEEE Transactions on Knowledge and Data Engineering profile →
  25. Efficient Algorithms for Mining High Utility Itemsets from Transactional Databases
    2012 380 citations Philip S. Yu et al. IEEE Transactions on Knowledge and Data Engineering 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. IEEE Transactions on Knowledge and Data Engineering 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 Philip S. Yu et al. 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 Philip S. Yu et al. profile →
  36. Improving automatic source code summarization via deep reinforcement learning
    2018 249 citations Philip S. Yu et al. profile →
  37. Spatial temporal incidence dynamic graph neural networks for traffic flow forecasting
    2020 243 citations Hao Peng, Lihong Wang et al. profile →
  38. A Survey on Heterogeneous Graph Embedding: Methods, Techniques, Applications and Sources
    2022 225 citations Philip S. Yu et al. profile →
  39. A Survey on Text Classification: From Traditional to Deep Learning
    2022 218 citations Hao Peng, Jianxin Li 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 Philip S. Yu, Lifang He et al. IEEE Transactions on Knowledge and Data Engineering profile →
  43. User Preference-aware Fake News Detection
    2021 152 citations Philip S. Yu et al. profile →
  44. Federated Social Recommendation with Graph Neural Network
    2022 116 citations Hao Peng, Philip S. Yu et al. profile →
  45. Decentralized Federated Learning: A Survey and Perspective
    2024 88 citations 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. IEEE Transactions on Knowledge and Data Engineering profile →
  47. Deep Clustering: A Comprehensive Survey
    2024 64 citations Philip S. Yu, Lifang He et al. 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 →

Peers

Philip S. Yu
Comparison fields: 5 of 224
  • Artificial Intelligence 43.0k
  • Information Systems 22.9k
  • Computer Networks and Communications 14.8k
  • Computer Vision and Pattern Recognition 12.5k
  • Signal Processing 12.4k
Replace Jiawei Han with:
Jiawei Han United States
Michael I. Jordan United States
Christos Faloutsos United States
Jürgen Schmidhuber Switzerland
Qiang Yang Hong Kong
George Karypis United States
Jure Leskovec United States
Jian Pei Canada
Andrew Y. Ng United States
Christopher D. Manning United States
Jiawei Han United States View profile →
Citations per field, relative to Philip S. Yu
Philip S. Yu · 1×
Citations per year, relative to Philip S. Yu
Philip S. Yu · 1×

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
# Work Indexed citations
1
Watermarking techniques for large language models: a survey
2026 ·Artificial Intelligence Review ·(unknown), (unknown), (unknown), Philip S. Yu
0
2
Knowledge Distillation in Federated Learning: A Survey on Long Lasting Challenges and New Solutions
2025 ·International Journal of Intelligent Systems ·(unknown), Tianqing Zhu, Wanlei Zhou, Philip S. Yu
1
3
Table-Critic: A Multi-Agent Framework for Collaborative Criticism and Refinement in Table Reasoning
2025 ·Philip S. Yu, Guoxin Chen, Jingjing Wang
0
4
MathAgent: Leveraging a Mixture-of-Math-Agent Framework for Real-World Multimodal Mathematical Error Detection
2025 ·(unknown), (unknown), (unknown), Philip S. Yu, (unknown), (unknown)
5
5
Emotion Transfer with Enhanced Prototype for Unseen Emotion Recognition in Conversation
2025 ·(unknown), (unknown), Hao Peng, Guanlin Wu, (unknown), (unknown), Yanbing Liu, Philip S. Yu
0
6
Online Learning for Noisy Labeled Streams
2025 ·ACM Transactions on Knowledge Discovery from Data ·(unknown), (unknown), Philip S. Yu, Chang‐Dong Wang, Shuqiang Huang
0
7
LLM Agents for Education: Advances and Applications
2025 ·(unknown), Shen Wang, Jianhe Xie, (unknown), (unknown), (unknown), (unknown), Xuming Hu, Jing Liang, Philip S. Yu, Qingsong Wen
4
8
A Contrastive-Enhanced Ensemble Framework for Efficient Multi-Agent Reinforcement Learning
2024 ·Expert Systems with Applications ·(unknown), Hechang Chen, Yongheng Xing, Philip S. Yu, Lifang He
6
9
Large language models in law: A survey breakdown →
2024 ·SHILAP Revista de lepidopterología ·(unknown), Wensheng Gan, Jiayang Wu, (unknown), Philip S. Yu
35
10
Slot Induction via Pre-trained Language Model Probing and Multi-level Contrastive Learning
2023 ·Hoang Nguyen, (unknown), (unknown), Philip S. Yu
1
11
Recent advances in domain-driven data mining
2022 ·International Journal of Data Science and Analytics ·Chuanren Liu, (unknown), Tong Xu, Philip S. Yu
3
12
Adaptive Pre-Training and Collaborative Fine-Tuning: A Win-Win Strategy to Improve Review Analysis Tasks
2022 ·IEEE/ACM Transactions on Audio Speech and Language Processing ·(unknown), Jianxin Li, Chenghua Lin, (unknown), Hao Peng, Lihong Wang, Philip S. Yu
2
13
Towards Revenue Maximization with Popular and Profitable Products
2021 ·Wensheng Gan, Guoting Chen, Hongzhi Yin, Philippe Fournier‐Viger, Chien‐Ming Chen, Philip S. Yu
1
14
SelfORE: Self-supervised Relational Feature Learning for Open Relation Extraction
2020 ·Xuming Hu, Lijie Wen, (unknown), Chenwei Zhang, Philip S. Yu
48
15
FakeDetector: Effective Fake News Detection with Deep Diffusive Neural Network
2020 ·Jiawei Zhang, Bowen Dong, Philip S. Yu
145
16
Hierarchical Taxonomy-Aware and Attentional Graph Capsule RCNNs for Large-Scale Multi-Label Text Classification
2019 ·IEEE Transactions on Knowledge and Data Engineering ·Hao Peng, Jianxin Li, Senzhang Wang, Lihong Wang, Qiran Gong, Renyu Yang, Bo Li, Philip S. Yu, Lifang He
124
17
Heterogeneous Information Network Embedding for Recommendation breakdown →
2018 ·IEEE Transactions on Knowledge and Data Engineering ·(unknown), Binbin Hu, Wayne Xin Zhao, Philip S. Yu
792
18
Spatiotemporal Pyramid Network for Video Action Recognition
2017 ·Yunbo Wang, Mingsheng Long, Jianmin Wang, Philip S. Yu
190
19
Review Graph Based Online Store Review Spammer Detection
2011 ·Guan Wang, Sihong Xie, Bing Liu, Philip S. Yu
243
20
Ownership protection of shape datasets with geodesic distance preservation
2008 ·ISTI Open Portal ·Michail Vlachos, Claudio Lucchese, Deepak Rajan, Philip S. Yu
3

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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