Collaboration Distance and Erdős Number Analysis
Collaboration distance analysis measures how closely connected scientists are through chains of co-authorship. Two researchers who have written a paper together are at distance 1; if they share a co-author but never wrote together, distance 2; and so on. The most famous instance is the Erdős number, the collaboration distance to the prolific mathematician Paul Erdős, popularized by the Erdős Number Project and analyzed by Rodrigo de Castro and Jerrold Grossman. M. E. J. Newman's landmark 2001 PNAS study generalized this idea, constructing large co-authorship networks across physics, biomedicine, and computer science and showing that they are 'small worlds': despite millions of authors, typical shortest paths are short and local clustering is high. Collaboration distance analysis thus characterizes the connectivity and reach of scientific communities through the geometry of their co-authorship graphs.
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출처
- Newman, M. E. J. (2001). The structure of scientific collaboration networks. Proceedings of the National Academy of Sciences, 98(2), 404-409. DOI: 10.1073/pnas.98.2.404 ↗
- De Castro, R., & Grossman, J. W. (1999). Famous trails to Paul Erdős. The Mathematical Intelligencer, 21(3), 51-63. DOI: 10.1007/BF03025416 ↗
이 페이지 인용 방법
ScholarGate. (2026, June 23). Collaboration Distance and Erdős Number Analysis (Geodesics in Co-Authorship Networks). ScholarGate. https://scholargate.app/ko/bibliometrics/collaboration-distance-analysis
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이 방법을 가장 가까운 동류의 방법들과 나란히 놓고 비교해 보세요 — 라이브러리는 책을 펼쳐 놓을 뿐, 선택은 여러분의 몫입니다.
- Garfield's Law of Concentration계량서지학↔ 비교
- Relative Specialization / Activity Index계량서지학↔ 비교
- Scientific Collaboration Index (Co-Authorship Intensity)계량서지학↔ 비교