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A new approach for detecting scientific specialties from raw cocitation networks

Authors

  • Matthew L. Wallace,

    1. Observatoire des sciences et des technologies (OST), Centre interuniversitaire de recherche sur la science et la technologie (CIRST), Université du Québec à Montréal, Case Postale 8888, succ. Centre-Ville, Montréal, Québec, H3C 3P8 Canada
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  • Yves Gingras,

    1. Observatoire des sciences et des technologies (OST), Centre interuniversitaire de recherche sur la science et la technologie (CIRST), Université du Québec à Montréal, Case Postale 8888, succ. Centre-Ville, Montréal, Québec, H3C 3P8 Canada
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  • Russell Duhon

    1. School of Library and Information Science, Indiana University, 1320 East 10th Street, LI 011, Bloomington, IN 47405–3907
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Abstract

We use a technique recently developed by V. Blondel, J.-L. Guillaume, R. Lambiotte, and E. Lefebvre (2008) to detect scientific specialties from author cocitation networks. This algorithm has distinct advantages over most previous methods used to obtain cocitation “clusters” since it avoids the use of similarity measures, relies entirely on the topology of the weighted network, and can be applied to relatively large networks. Most importantly, it requires no subjective interpretation of the cocitation data or of the communities found. Using two examples, we show that the resulting specialties are the smallest coherent “groups” of researchers (within a hierarchy of cluster sizes) and can thus be identified unambiguously. Furthermore, we confirm that these communities are indeed representative of what we know about the structure of a given scientific discipline and that as specialties, they can be accurately characterized by a few keywords (from the publication titles). We argue that this robust and efficient algorithm is particularly well-suited to cocitation networks and that the results generated can be of great use to researchers studying various facets of the structure and evolution of science.

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