Discovering Research Communities by Clustering Bibliographical Data

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-bibliographical data; graph-based clustering; community mining.

• Type: Journal
• Year: 2010
• Where: WEBI
• Authors: Fabrice Muhlenbach, Stéphane Lallich
• Keywords: Graph-based Clustering, Internet Technology, Mining, Social Network, WEBI 2010