Combining relations and text in scientific network clustering

David Combe (1) , Christine Largeron (1) , Előd Egyed-Zsigmond (2) , Mathias Géry (1)

1 LHC - Laboratoire Hubert Curien
2 DRIM - Distribution, Recherche d'Information et Mobilité

In this paper, we present different combined cluster- ing methods and we evaluate their performances and their results on a dataset with ground truth. This dataset, built from several sources, contains a scientific social network in which textual data is associated to each vertex and the classes are known. Indeed, while the clustering task is widely studied both in graph clustering and in non supervised learning, combined clustering which exploits simultaneously the relationships between the vertices and attributes describing them, is quite new. We argue that, depending on the kind of data we have and the type of results we want, the choice of the clustering method is important and we present some concrete examples for underlining this.

Domains: Information Retrieval [cs.IR]

David Combe, Christine Largeron, Előd Egyed-Zsigmond, Mathias Géry. Combining relations and text in scientific network clustering. 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, Aug 2012, Istanbul, Turkey. pp.1280-1285, ⟨10.1109/ASONAM.2012.215⟩. ⟨hal-00730226⟩