eprintid: 1114
rev_number: 7
eprint_status: archive
userid: 6
dir: disk0/00/00/11/14
datestamp: 2012-02-13 14:30:32
lastmod: 2013-11-21 09:04:26
status_changed: 2012-02-13 14:30:32
type: article
metadata_visibility: show
creators_name: Capocci, Andrea
creators_name: Servedio, Vito D. P.
creators_name: Caldarelli, Guido
creators_name: Colaiori, Francesca
creators_id: 
creators_id: 
creators_id: guido.caldarelli@imtlucca.it
creators_id: 
title: Detecting communities in large networks
ispublished: pub
subjects: QA
subjects: QA75
divisions: EIC
full_text_status: none
keywords: PACS: 89.75.Hc; 89.75.Da; 89.75.Fb; Keywords: Networks and genealogical trees; Systems obeying scaling laws; Structures and organization in complex systems
abstract: We develop an algorithm to detect community structure in complex networks. The algorithm is based on spectral methods and takes into account weights and link orientation. Since the method detects efficiently clustered nodes in large networks even when these are not sharply partitioned, it turns to be specially suitable for the analysis of social and information networks. We test the algorithm on a large-scale data-set from a psychological experiment of word association. In this case, it proves to be successful both in clustering words, and in uncovering mental association patterns.
date: 2005-01
date_type: published
publication: Physica A: Statistical Mechanics and its Applications
volume: 352
number: 2-4
publisher: Elsevier
pagerange: 669-676
id_number: 10.1016/j.physa.2004.12.050
refereed: TRUE
issn: 0378-4371
official_url: http://dx.doi.org/10.1016/j.physa.2004.12.050
related_url_url: http://arxiv.org/abs/cond-mat/0402499
citation:   Capocci, Andrea and Servedio, Vito D. P. and Caldarelli, Guido and Colaiori, Francesca  Detecting communities in large networks.  Physica A: Statistical Mechanics and its Applications, 352 (2-4).  pp. 669-676.  ISSN 0378-4371      (2005)