Graph Clustering Using Early-Stopped Random Walks - Inria - Institut national de recherche en sciences et technologies du numérique Accéder directement au contenu
Communication Dans Un Congrès Année : 2016

Graph Clustering Using Early-Stopped Random Walks

Résumé

Very fast growth of empirical graphs demands clustering algorithms with nearly-linear time complexity. We propose a novel approach to clustering, based on random walks. The idea is to relax the standard spectral method and replace eigenvectors with vectors obtained by running early-stopped random walks. We abandoned iterating the random walk algorithm to convergence but instead stopped it after the time that is short compared with the mixing time. The computed vectors constitute a local approximation of the leading eigenvectors. The algorithm performance is competitive to the traditional spectral solutions in terms of computational complexity. We empirically evaluate the proposed approach against other exact and approximate methods. Experimental results show that the use of the early stop procedure does not influence the quality of the clustering on the tested real world data sets.
Fichier principal
Vignette du fichier
419526_1_En_37_Chapter.pdf (94.91 Ko) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)
Loading...

Dates et versions

hal-01637482 , version 1 (17-11-2017)

Licence

Paternité

Identifiants

Citer

Małgorzata Lucińska, Sławomir T. Wierzchoń. Graph Clustering Using Early-Stopped Random Walks. 15th IFIP International Conference on Computer Information Systems and Industrial Management (CISIM), Sep 2016, Vilnius, Lithuania. pp.416-428, ⟨10.1007/978-3-319-45378-1_37⟩. ⟨hal-01637482⟩
107 Consultations
183 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More