Node-Centric Community Detection in Multilayer Networks with Layer-Coverage Diversification Bias - LIRMM - Laboratoire d’Informatique, de Robotique et de Microélectronique de Montpellier Access content directly
Conference Papers Year : 2017

Node-Centric Community Detection in Multilayer Networks with Layer-Coverage Diversification Bias

Abstract

The problem of node-centric, or local, community detection in information networks refers to the identification of a community for a given input node, having limited information about the network topology. Existing methods for solving this problem, however, are not conceived to work on complex networks. In this paper, we propose a novel framework for local community detection based on the multilayer network model. Our approach relies on the maximization of the ratio between the community internal connection density and the external connection density , according to multilayer similarity-based community relations. We also define a biasing scheme that allows the discovery of local communities characterized by different degrees of layer-coverage diversification. Experimental evaluation conducted on real-world multilayer networks has shown the significance of our approach.
Fichier principal
Vignette du fichier
interdonato_al_2017.pdf (284.68 Ko) Télécharger le fichier
Origin Publisher files allowed on an open archive
Loading...

Dates and versions

lirmm-01912004 , version 1 (05-11-2018)

Identifiers

Cite

Roberto Interdonato, Andrea Tagarelli, Dino Ienco, Arnaud Sallaberry, Pascal Poncelet. Node-Centric Community Detection in Multilayer Networks with Layer-Coverage Diversification Bias. CompleNet, Mar 2017, Dubrovnik, Croatia. pp.57-66, ⟨10.1007/978-3-319-54241-6_5⟩. ⟨lirmm-01912004⟩
167 View
112 Download

Altmetric

Share

Gmail Mastodon Facebook X LinkedIn More