A comparison of graph clustering algorithms - Archive ouverte HAL Accéder directement au contenu
Communication Dans Un Congrès Année : 2015

A comparison of graph clustering algorithms

Résumé

The community detection problem is very natural : given a set of people and their relationships, can we understand the underlying structure of social groups? The applications are numerous in market- ing, politics, social statistics, . . . Thanks to technological improvements and change of uses, many social networks of various sizes has been automatically extracted from recorded social relationships. The most obvious examples are the social network websites, but other networks are also studied as social networks : collabora- tion between scientists, who-talks-to-whom on the phone/on emails, etc. Automatic extraction made large networks available for study, and the com- munity detection algorithms that we could evaluate with ease by watching the result on small instances before, can not be compared on real-world networks. We therefore try to find common ground among the various clustering algorithms. Indeed, most of them share design similarities, as the underlying as- sumptions about the characteristics of communities or the general steps of the algorithm. Our experi- ments show to what extent these similarities imply similarities of results. Our work is close to the one of Almeida et al. [1], that compared a good number of algorithms and a few quality functions. However, they did not di- rectly compare clusterings, but only the result when a quality function is applied on them.
Fichier principal
Vignette du fichier
poster7.pdf (175.67 Ko) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)
Loading...

Dates et versions

hal-01171341 , version 1 (03-07-2015)

Identifiants

  • HAL Id : hal-01171341 , version 1

Citer

Jean Creusefond. A comparison of graph clustering algorithms. International Symposium on Web AlGorithms, Jun 2015, Deauville, France. ⟨hal-01171341⟩
135 Consultations
404 Téléchargements

Partager

Gmail Facebook X LinkedIn More