Scientific collaboration networks. II. Shortest paths, weighted networks, and centrality

2000/11/28 by M. E. J. Newman · 21 citations
Biochemistry, Genetics and Molecular Biology · Computer Science · Mathematics · Physics and Astronomy · #Artificial intelligence #Betweenness centrality #Bioinformatics and Genomic Networks #Centrality #Closeness #Combinatorics #Complex Network Analysis Techniques #Complex network #Computer science #Data mining #Data science #Mathematics #Measure (data warehouse) #Network science #Peer-to-Peer Network Technologies #Theoretical computer science #Variety (cybernetics) #World Wide Web #cond-mat.stat-mech #physics.soc-ph

paper · pdf · doi:10.1103/physreve.64.016132

published as Phys.Rev. E64 (2001) 016131; Phys.Rev. E64 (2001) 016132 · 17 pages, 10 figures, 3 tables. Minor corrections and updates in this version. Accompanying material can be found on the web at http://www.santafe.edu/~mark/collaboration/

arxiv created 2000/11/28 · openalex publication_date 2001/06/28 · arxiv updated 2009/11/30 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05

Abstract

Using computer databases of scientific papers in physics, biomedical research, and computer science, we have constructed networks of collaboration between scientists in each of these disciplines. In these networks two scientists are considered connected if they have coauthored one or more papers together. Here we study a variety of nonlocal statistics for these networks, such as typical distances between scientists through the network, and measures of centrality such as closeness and betweenness. We further argue that simple networks such as these cannot capture variation in the strength of collaborative ties and propose a measure of collaboration strength based on the number of papers coauthored by pairs of scientists, and the number of other scientists with whom they coauthored those papers.

Citations

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