Analysis of the time evolution of scientograms using the subdue graph mining algorithm
IPMU'10 Proceedings of the Computational intelligence for knowledge-based systems design, and 13th international conference on Information processing and management of uncertainty
A comparison of mapping algorithms for author co-citation data analysis
Proceedings of the 73rd ASIS&T Annual Meeting on Navigating Streams in an Information Ecosystem - Volume 47
Multi-modal social networks for modeling scientific fields
Scientometrics
A new methodology for constructing a publication-level classification system of science
Journal of the American Society for Information Science and Technology
Competence maps using agglomerative hierarchical clustering
Journal of Intelligent Manufacturing
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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. © 2009 Wiley Periodicals, Inc.