Suhang Wang: h-index, Total Citations, and Citation Map
Suhang Wang's h-index is 68 (165 i10-index, 30,795+ total citations across 250+ publications) according to Google Scholar as of May 2026. Suhang Wang is affiliated with Pennsylvania State University.
Suhang Wang is a researcher affiliated with Pennsylvania State University, specializing in Data mining, Machine learning, Deep Learning. Their work has been cited 30,795 times. This profile visualizes their global influence, highlighting strong citation networks in China.
Suhang Wang's Citation Metrics
Bibliometric impact based on 250 indexed publications. Of these, 17 are original research articles — the rest are literature highlights, conference abstracts or theses.
- H-Index
- 68
- i10-Index
- 165
- Total Citations
- 30,795
- Citing Countries
- 77
As of May 2026.
Suhang Wang has an h-index of 68 and 30,795 total citations across 250 publications, with research cited by institutions in 77 countries.
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We've mapped 5,000 of 30,795 citations for Suhang Wang
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Global Impact Map
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Top Cited Works
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Fake News Detection on Social Media: A Data Mining Perspective
20175,233
Top Citing Countries
Top Citing Institutions
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.
783 citing papers could not be classified (no author data) — excluded from the percentages above.
The researcher established a foundational data mining framework for fake news detection, subsequently expanding it through curated repositories and hierarchical propagation models.
The researcher pioneered graph structure learning for robust GNNs, establishing a foundational framework later expanded into a comprehensive survey on trustworthy graph neural networks.
The researcher established a comprehensive data-centric framework for feature selection, synthesizing existing methods and identifying critical research gaps in a seminal survey widely recognized as a foundational reference in the field.
Citation trend (last 10 years)Click to expand
Citation Trend (Last 10 Years)
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