Xiao Zhang: h-index, Total Citations, and Citation Map
Xiao Zhang's h-index is 6 (6 i10-index, 1,436+ total citations across 5+ publications) according to Google Scholar as of May 2026. Xiao Zhang is affiliated with Amazon.
Xiao Zhang is a researcher affiliated with Amazon, specializing in network science, complex system, physics. Their work has been cited 1,436 times. This profile visualizes their global influence, highlighting strong citation networks in United States.
Xiao Zhang's Citation Metrics
Bibliometric impact based on 5 indexed publications. Of these, 2 are original research articles — the rest are literature highlights, conference abstracts or theses.
- H-Index
- 6
- i10-Index
- 6
- Total Citations
- 1,436
- Citing Countries
- 22
As of May 2026.
Xiao Zhang has an h-index of 6 and 1,436 total citations across 5 publications, with research cited by institutions in 22 countries.
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Global Impact Map
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Top Cited Works
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Identification of core-periphery structure in networks
2015630
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.
The researcher developed a method for identifying core-periphery structures in networks, a foundational contribution evidenced by high citation counts and widespread independent adoption.
The researcher advanced network science by developing frameworks to analyze localization and centrality, establishing a foundational reference point for understanding structural properties in complex systems.
The researcher developed random graph models for dynamic networks, a foundational framework cited 194 times by independent scholars, advancing the theoretical understanding of evolving complex systems.
Citation trend (last 10 years)Click to expand
Citation Trend (Last 10 Years)
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