Kave Salamatian: h-index, Total Citations, and Citation Map
Kave Salamatian's h-index is 35 (95 i10-index, 7,247+ total citations across 210+ publications) according to Google Scholar as of May 2026. Kave Salamatian is affiliated with LISTIC, Universite de Savoie, LISTIC.
Kave Salamatian is a researcher affiliated with LISTIC, Universite de Savoie, LISTIC, specializing in Cybersecurity, networking, Internet Science. Their work has been cited 7,247 times. This profile visualizes their global influence, highlighting strong citation networks in United States.
Kave Salamatian's Citation Metrics
Bibliometric impact based on 210 indexed publications. Of these, 15 are original research articles — the rest are literature highlights, conference abstracts or theses.
- H-Index
- 35
- i10-Index
- 95
- Total Citations
- 7,247
- Citing Countries
- 37
As of May 2026.
Kave Salamatian has an h-index of 35 and 7,247 total citations across 210 publications, with research cited by institutions in 37 countries.
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We've mapped 5,000 of 7,247 citations for Kave Salamatian
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Global Impact Map
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Top Cited Works
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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.
2376 citing papers could not be classified (no author data) — excluded from the percentages above.
The researcher established a foundational framework for traffic matrix estimation, synthesizing existing techniques and defining new research directions that have significantly influenced network measurement and inference methodologies.
The researcher developed a foundational framework for network anomaly detection by integrating filtering and statistical methods, subsequently extending this approach to address specific challenges in traffic analysis and backbone networks.
The researcher developed foundational Hidden Markov Modeling techniques for network communication channels, establishing a methodological framework for analyzing complex network traffic patterns.
Citation trend (last 10 years)Click to expand
Citation Trend (Last 10 Years)
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