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We use a technique recently developed by Blondel et al. (2008) in order todetect scientific specialties from author cocitation networks. This algorithmhas distinct advantages over most of the previous methods used to obtaincocitation "clusters", since it avoids the use of similarity measures, reliesentirely on the topology of the weighted network and can be applied torelatively large networks. Most importantly, it requires no subjectiveinterpretation of the cocitation data or of the communities found. Using twoexamples, we show that the resulting specialties are the smallest coherent"group" of researchers (within a hierarchy of cluster sizes) and can thus beidentified unambiguously. Furthermore, we confirm that these communities areindeed representative of what we know about the structure of a given scientificdiscipline and that, as specialties, they can be accurately characterized by afew keywords (from the publication titles). We argue that this robust andefficient algorithm is particularly well-suited to cocitation networks, andthat the results generated can be of great use to researchers studying variousfacets of the structure and evolution of science.

A new approach for detecting scientific specialties from raw cocitation networks