TY - CHAP UR - http://dx.doi.org/10.1007/978-3-540-30216-2_15 Y1 - 2004/10// AV - none SP - 181 ID - eprints1113 EP - 187 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 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. T3 - Lecture Notes in Computer Science TI - Communities Detection in Large Networks SN - 0302-9743 PB - Springer-Verlag T2 - Algorithms and Models for the Web-Graph A1 - Capocci, Andrea A1 - Servedio, Vito D. P. A1 - Caldarelli, Guido A1 - Colaiori, Francesca ED - Leonardi, Stefano N1 - Proceeedings of the Third International Workshop (WAW 2004) Rome, Italy, October 16, 2004. ER -