Philip S. Yu
- Artificial Intelligence top 0.01%
- Information Systems top 0.01%
- Computer Networks and Communications top 0.01%
- Computer Vision and Pattern Recognition top 0.02%
- Signal Processing top 0.01%
- Co-authors
- Charų C. AggarwalMing-Syan Chen⋆Jiawei HanJianmin WangMingsheng LongHaixun WangJong Soo ParkJoel L. Wolf
- Topics
- Advanced Graph Neural Networks (279 papers)Complex Network Analysis Techniques (251 papers)Data Management and Algorithms (223 papers)
- Journals
- Nucleic Acids ResearchSHILAP Revista de lepidopterologíaBioinformatics
- Partner nations
- United StatesChinaAustralia
In The Last Decade
Philip S. Yu
1.5k papers receiving 71.6k citations
Hit Papers
Peers
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
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-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
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 1 | |
| 3 | 0 | |
| 4 | 5 | |
| 5 | 0 | |
| 6 | 0 | |
| 7 | 4 | |
| 8 | 6 | |
| 9 | Large language models in law: A surveybreakdown → | 35 |
| 10 | 1 | |
| 11 | 3 | |
| 12 | 2 | |
| 13 | 1 | |
| 14 | 48 | |
| 15 | 145 | |
| 16 | 124 | |
| 17 | Heterogeneous Information Network Embedding for Recommendationbreakdown → | 792 |
| 18 | 190 | |
| 19 | 243 | |
| 20 | 3 |
About Philip S. Yu
Philip S. Yu is a scholar working on Computational Mathematics, Artificial Intelligence and Signal Processing, having authored 1.6k papers that have together received 75.6k indexed citations. Recurring topics across this work include Advanced Graph Neural Networks (279 papers), Complex Network Analysis Techniques (251 papers) and Data Management and Algorithms (223 papers). The work is most often cited by research in Artificial Intelligence (43.0k citations), Signal Processing (12.4k citations) and Information Systems (22.9k citations). Philip S. Yu has collaborated with scholars based in United States, China and Australia. Frequent 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. Their work appears in journals such as Nucleic Acids Research, SHILAP Revista de lepidopterología and Bioinformatics.
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.