Philip S. Yu
Impact in
- Artificial Intelligence top 0.01%
- Advanced Graph Neural Networks
- Privacy-Preserving Technologies in Data
- Topic Modeling
- Data Stream Mining Techniques
- Signal Processing top 0.01%
- Data Management and Algorithms
Papers in
-
- Advanced Graph Neural Networks 317
- Topic Modeling 184
-
- Data Mining Algorithms and Applications 216
- Recommender Systems and Techniques 160
- Co-authors
- Charų C. Aggarwal (104 shared papers)Jiawei Han (46 shared papers)Ming-Syan Chen⋆ (81 shared papers)Jianmin Wang (37 shared papers)Haixun Wang (48 shared papers)Jong Soo Park (9 shared papers)Mingsheng Long (21 shared papers)Joel L. Wolf (58 shared papers)
- Journals
- IEEE Transactions on Knowledge and Data Engineering (145 papers)Knowledge and Information Systems (33 papers)ACM Transactions on Knowledge Discovery from Data (23 papers)IEEE Transactions on Neural Networks and Learning Systems (22 papers)IEEE Transactions on Parallel and Distributed Systems (18 papers)
- Partner nations
- United StatesChinaAustralia
In The Last Decade
Philip S. Yu
1.7k papers receiving 87.8k citations
Philip S. Yu's Hit Papers
Peers
Comparison fields: 5 of 225
- Artificial Intelligence 53.3k
- Signal Processing 16.3k
- Information Systems 28.4k
- Computational Mathematics 566
- Computer Networks and Communications 18.2k
Countries citing papers authored by Philip S. Yu
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
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-authors
The 25 scholars most cited alongside Philip S. Yu, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 1.8k papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | Top 10 algorithms in data mining Hit paper breakdown → | 2007 | 4439 |
| 2 | A Survey on Knowledge Graphs: Representation, Acquisition, and Applications Hit paper breakdown → | 2021 | 1656 |
| 3 | Data mining: an overview from a database perspective Hit paper breakdown → | 1996 | 1654 |
| 4 | Transfer Feature Learning with Joint Distribution Adaptation Hit paper breakdown → | 2013 | 1548 |
| 5 | PathSim Hit paper breakdown → | 2011 | 1406 |
| 6 | A Framework for Clustering Evolving Data Streams Hit paper breakdown → | 2003 | 1379 |
| 7 | A Survey on Evaluation of Large Language Models Hit paper breakdown → | 2024 | 1322 |
| 8 | Privacy-preserving data publishing Hit paper breakdown → | 2010 | 1249 |
| 9 | An effective hash-based algorithm for mining association rules Hit paper breakdown → | 1995 | 1092 |
| 10 | A holistic lexicon-based approach to opinion mining Hit paper breakdown → | 2008 | 1074 |
| 11 | Mining concept-drifting data streams using ensemble classifiers Hit paper breakdown → | 2003 | 997 |
| 12 | A new method to measure the semantic similarity of GO terms Hit paper breakdown → | 2007 | 961 |
| 13 | Heterogeneous Information Network Embedding for Recommendation Hit paper breakdown → | 2018 | 876 |
| 14 | Outlier detection for high dimensional data Hit paper breakdown → | 2001 | 813 |
| 15 | A survey of heterogeneous information network analysis Hit paper breakdown → | 2016 | 785 |
| 16 | Fast algorithms for projected clustering Hit paper breakdown → | 1999 | 740 |
| 17 | Generalizing to Unseen Domains: A Survey on Domain Generalization Hit paper breakdown → | 2022 | 645 |
| 18 | Transfer Joint Matching for Unsupervised Domain Adaptation Hit paper breakdown → | 2014 | 564 |
| 19 | Building text classifiers using positive and unlabeled examples Hit paper breakdown → | 2004 | 530 |
| 20 | Adaptation Regularization: A General Framework for Transfer Learning Hit paper breakdown → | 2013 | 518 |
About Philip S. Yu
Philip S. Yu is a scholar working on Artificial Intelligence, Information Systems, Computer Networks and Communications, Signal Processing and Statistical and Nonlinear Physics, having authored 1.8k papers that have together received 92.6k indexed citations. Recurring topics across this work include Advanced Graph Neural Networks (317 papers), Complex Network Analysis Techniques (295 papers), Data Management and Algorithms (247 papers), Data Mining Algorithms and Applications (216 papers), Topic Modeling (184 papers), Recommender Systems and Techniques (160 papers), Advanced Database Systems and Queries (141 papers) and Caching and Content Delivery (111 papers). The work is most often cited by research in Artificial Intelligence (53.3k citations), Signal Processing (16.3k citations), Information Systems (28.4k citations), Computational Mathematics (566 citations) and Computer Networks and Communications (18.2k citations). Philip S. Yu has collaborated with scholars based in United States, China and Australia. Frequent co-authors include Charų C. Aggarwal, Jiawei Han, Ming-Syan Chen⋆, Jianmin Wang, Haixun Wang, Jong Soo Park, Mingsheng Long, Joel L. Wolf, Xifeng Yan and Bing Liu. Their work appears in journals such as IEEE Transactions on Knowledge and Data Engineering, Knowledge and Information Systems, ACM Transactions on Knowledge Discovery from Data, IEEE Transactions on Neural Networks and Learning Systems and IEEE Transactions on Parallel and Distributed Systems.
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.