Philip S. Yu: h-index, Total Citations, and Citation Map
Philip S. Yu's h-index is 217 (1796 i10-index, 259,669+ total citations across 6+ publications) according to Google Scholar as of May 2026. Philip S. Yu is affiliated with Professor of Computer Science, University of Illinons at Chicago.
Philip S. Yu is a researcher affiliated with Professor of Computer Science, University of Illinons at Chicago, specializing in Data mining, Database, Privacy. Their work has been cited 259,669 times. This profile visualizes their global influence, highlighting strong citation networks in United States.
Philip S. Yu's Citation Metrics
Bibliometric impact based on 6 indexed publications. Of these, 3 are original research articles — the rest are literature highlights, conference abstracts or theses.
- H-Index
- 217
- i10-Index
- 1796
- Total Citations
- 259,669
- Citing Countries
- 24
As of May 2026.
Philip S. Yu has an h-index of 217 and 259,669 total citations across 6 publications, with research cited by institutions in 24 countries.
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Top Cited Works
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A Comprehensive Survey on Graph Neural Networks
202016,103
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Visa Evidence Package
Views and exports tuned for EB-1A, O-1A, and EB-2 NIW petitions. Sustained acclaim, geographic reach, and independent-citation filtering are the strongest evidence categories immigration adjudicators look for.
Significant Contributions
Auto-detected research lines — a seminal paper and the follow-up work building on it. Review and edit before using in a petition. Each Free PDF opens in a new tab — EB-1A organises this into the structure USCIS applies to Criterion 5 of 8 CFR § 204.5(h)(3)(v); EB-1B re-frames it under § 204.5(i)(3) (outstanding researcher); NIW presents it under prong 2 of Matter of Dhanasar.
The researcher provided a foundational database-centric overview of data mining, establishing a seminal framework that has been widely adopted by the independent academic community.
The researcher established a foundational benchmark by identifying and analyzing the top ten algorithms in data mining, creating a widely adopted reference point for the field.
The researcher established a foundational framework for Graph Neural Networks through a comprehensive survey that systematically organized the field's theoretical foundations and practical applications.
Citation trend (last 10 years)Click to expand
Citation Trend (Last 10 Years)
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