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.
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Capocci, A., Servedio, V.D.P., Caldarelli, G., Colaiori, F. (2004). Communities Detection in Large Networks. In: Leonardi, S. (eds) Algorithms and Models for the Web-Graph. WAW 2004. Lecture Notes in Computer Science, vol 3243. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30216-2_15
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DOI: https://doi.org/10.1007/978-3-540-30216-2_15
Publisher Name: Springer, Berlin, Heidelberg
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