Amir Ghasemian: h-index, Total Citations, and Citation Map
Amir Ghasemian's h-index is 10 (10 i10-index, 1,197+ total citations across 4+ publications) according to Google Scholar as of May 2026. Amir Ghasemian is affiliated with Researcher at UCLA | Co-Founder of the OASIS Lab.
Amir Ghasemian is a researcher affiliated with Researcher at UCLA | Co-Founder of the OASIS Lab, specializing in Network Science, Machine Learning, Causal Inference. Their work has been cited 1,197 times. This profile visualizes their global influence, highlighting strong citation networks in United States.
Amir Ghasemian's Citation Metrics
Bibliometric impact based on 4 indexed publications. Of these, 3 are original research articles — the rest are literature highlights, conference abstracts or theses.
- H-Index
- 10
- i10-Index
- 10
- Total Citations
- 1,197
- Citing Countries
- 14
As of May 2026.
Amir Ghasemian has an h-index of 10 and 1,197 total citations across 4 publications, with research cited by institutions in 14 countries.
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Global Impact Map
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Top Cited Works
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Examining the consumption of radical content on YouTube
2021263
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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 advanced the empirical understanding of radical content consumption on YouTube through a seminal 2021 study that has garnered significant independent scholarly attention.
The researcher established a rigorous framework for evaluating overfit and underfit in network community structure models, providing critical diagnostic tools for assessing model validity in complex network analysis.
The researcher established foundational detectability thresholds and optimal algorithms for identifying community structure in dynamic networks, as demonstrated in a seminal 2016 Physical Review X paper.
Citation trend (last 10 years)Click to expand
Citation Trend (Last 10 Years)
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