Duncan J Watts: h-index, Total Citations, and Citation Map
Duncan J Watts's h-index is 74 (126 i10-index, 147,774+ total citations across 3+ publications) according to Google Scholar as of May 2026. Duncan J Watts is affiliated with University of Pennsylvania.
Duncan J Watts is a researcher affiliated with University of Pennsylvania, specializing in Computational Social Science, Network Science, Sociology. Their work has been cited 147,774 times. This profile visualizes their global influence, highlighting strong citation networks in United States.
Duncan J Watts's Citation Metrics
Bibliometric impact based on 3 indexed publications. Of these, 2 are original research articles — the rest are literature highlights, conference abstracts or theses.
- H-Index
- 74
- i10-Index
- 126
- Total Citations
- 147,774
- Citing Countries
- 17
As of May 2026.
Duncan J Watts has an h-index of 74 and 147,774 total citations across 3 publications, with research cited by institutions in 17 countries.
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We've mapped 5,000 of 147,774 citations for Duncan J Watts
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Global Impact Map
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Top Cited Works
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Collective dynamics of 'small-world' networks
199859,624
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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 established the foundational framework for understanding collective dynamics in small-world networks, a seminal contribution that redefined the study of complex network structures.
The researcher established a foundational framework for understanding network dynamics between order and randomness, as evidenced by a seminal 1999 paper with over 7,000 citations.
The researcher established a foundational framework for modeling random graphs with arbitrary degree distributions, enabling the accurate analysis of complex network structures in physical and social systems.
Citation trend (last 10 years)Click to expand
Citation Trend (Last 10 Years)
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