Cosma Rohilla Shalizi: h-index, Total Citations, and Citation Map
Cosma Rohilla Shalizi's h-index is 41 (61 i10-index, 20,634+ total citations across 2+ publications) according to Google Scholar as of May 2026. Cosma Rohilla Shalizi is affiliated with Professor of Statistics, Carnegie Mellon University.
Cosma Rohilla Shalizi is a researcher affiliated with Professor of Statistics, Carnegie Mellon University, specializing in various fields. Their work has been cited 20,634 times. This profile visualizes their global influence, highlighting strong citation networks in United States.
Cosma Rohilla Shalizi's Citation Metrics
Bibliometric impact based on 2 indexed publications. Of these, 1 are original research articles — the rest are literature highlights, conference abstracts or theses.
- H-Index
- 41
- i10-Index
- 61
- Total Citations
- 20,634
- Citing Countries
- 7
As of May 2026.
Cosma Rohilla Shalizi has an h-index of 41 and 20,634 total citations across 2 publications, with research cited by institutions in 7 countries.
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Top Cited Works
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Power-law distributions in empirical data
200912,725
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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 established a foundational framework for analyzing power-law distributions in empirical data, providing a seminal reference that has become central to statistical methodology across diverse scientific fields.
The researcher demonstrated that homophily and contagion are generically confounded in observational social network studies, establishing a critical methodological constraint for causal inference in network analysis.
Citation trend (last 10 years)Click to expand
Citation Trend (Last 10 Years)
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