Discovering Research Communities by Clustering Bibliographical Data

Fabrice Muhlenbach (1) , Stéphane Lallich (2)

1 LHC - Laboratoire Hubert Curien
2 ERIC - Equipe de Recherche en Ingénierie des Connaissances

Today's world is characterized by the multiplicity of interconnections through many types of links between the people, that is why mining social networks appears to be an important topic. Extracting information from social networks becomes a challenging problem, particularly in the case of the discovery of community structures. Mining bibliographical data can be useful to find communities of researchers. In this paper we propose a formal definition to consider the similarity and dissimilarity between individuals of a social network and how a graph-based clustering method can extract research communities from the DBLP database.

Keywords: Social Network; Community Mining; Clustering; Neighborhood Graphs

Domains: Machine Learning [cs.LG]

Fabrice Muhlenbach, Stéphane Lallich. Discovering Research Communities by Clustering Bibliographical Data. IEEE/WIC/ACM International Conference on Web Intelligence, WI 2010, Aug 2010, Toronto, Canada. pp.500-507. ⟨hal-00516610⟩