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What is the difference of research collaboration network under different projections: Topological measurement and analysis

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  • Li, Yongjun
  • You, Chun

Abstract

Research collaboration network is a typical bipartite network that consists of papers and authors. This bipartite network could be transformed into one-mode networks by projection. In this paper, we used three different projections to construct three co-authorship networks. Topological features of three co-authorship networks are measured and analyzed in order to understand the influence of projections on network features. The measurement results show that different projections could lead to different topological features. Therefore, to reflect the existing reality more precisely, projection method is suggested to be considered when we investigate the structure of scientific collaborations and/or assess the status, impact and influence of researchers and their institutions.

Suggested Citation

  • Li, Yongjun & You, Chun, 2013. "What is the difference of research collaboration network under different projections: Topological measurement and analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(15), pages 3248-3259.
    • Handle: RePEc:eee:phsmap:v:392:y:2013:i:15:p:3248-3259
    DOI: 10.1016/j.physa.2013.03.021
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    1. Cardillo, Alessio & Scellato, Salvatore & Latora, Vito, 2006. "A topological analysis of scientific coauthorship networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 372(2), pages 333-339.
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    4. Barabási, A.L & Jeong, H & Néda, Z & Ravasz, E & Schubert, A & Vicsek, T, 2002. "Evolution of the social network of scientific collaborations," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 311(3), pages 590-614.
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    Cited by:

    1. Leifeld, Philip, 2018. "Polarization in the social sciences: Assortative mixing in social science collaboration networks is resilient to interventions," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 507(C), pages 510-523.
    2. Arthur, Rudy, 2020. "Modularity and projection of bipartite networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 549(C).
    3. Rodica Ioana Lung & Noémi Gaskó & Mihai Alexandru Suciu, 2018. "A hypergraph model for representing scientific output," Scientometrics, Springer;Akadémiai Kiadó, vol. 117(3), pages 1361-1379, December.

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