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