@article{citeulike:1001736,
title = {Detecting communities in large networks},
author = {A. Capocci and V. D. P. Servedio and G. Caldarelli and F. Colaiori},
journal = {Physica A: Statistical and Theoretical Physics},
month = {July},
number = {2-4},
pages = {669--676},
url = {http://www.sciencedirect.com/science/article/B6TVG-4FB91CD-8/2/508224d0d5e1fc0635dbaf18ee058541},
volume = {352},
year = {2005},
description = {gcalda's book},
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.},
doi = {10.1016/j.physa.2004.12.050}, citeulike-article-id = {1001736}, priority = {0},
keywords = {community graphtheory mining smallworld }
}
::