eprintid: 1113
rev_number: 8
eprint_status: archive
userid: 6
dir: disk0/00/00/11/13
datestamp: 2012-02-13 14:15:03
lastmod: 2012-02-13 14:15:03
status_changed: 2012-02-13 14:15:03
type: book_section
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: Communities Detection in Large Networks
ispublished: pub
subjects: QA
subjects: QA75
divisions: EIC
full_text_status: none
note: Proceeedings of the Third International Workshop (WAW 2004) Rome, Italy, October 16, 2004. 
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 links orientations. Since the method detects efficiently clustered nodes in large networks even when these are not sharply partitioned, it turns to be specially suitable to 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: 2004-10
date_type: published
series: Lecture Notes in Computer Science
number: 3243
publisher: Springer-Verlag
pagerange: 181-187
id_number: 10.1007/978-3-540-30216-2_15
refereed: TRUE
isbn: 978-3-540-23427-2
issn: 0302-9743
book_title: Algorithms and Models for the Web-Graph
editors_name: Leonardi, Stefano
official_url: http://dx.doi.org/10.1007/978-3-540-30216-2_15
citation:   Capocci, Andrea and Servedio, Vito D. P. and Caldarelli, Guido and Colaiori, Francesca  Communities Detection in Large Networks.   In:  Algorithms and Models for the Web-Graph.   Lecture Notes in Computer Science  (3243).  Springer-Verlag, pp. 181-187.  ISBN 978-3-540-23427-2      (2004)