TY  - JOUR
SN  - 0378-4371
N2  - 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.
ID  - eprints1114
EP  - 676
PB  - Elsevier
JF  - Physica A: Statistical Mechanics and its Applications
IS  - 2-4
AV  - none
SP  - 669
TI  - Detecting communities in large networks
VL  - 352
UR  - http://dx.doi.org/10.1016/j.physa.2004.12.050
A1  - Capocci, Andrea
A1  - Servedio, Vito D. P.
A1  - Caldarelli, Guido
A1  - Colaiori, Francesca
Y1  - 2005/01//
KW  - 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
ER  -