Clustering in weighted networks |
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Authors: | Tore Opsahl Pietro Panzarasa |
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Affiliation: | Queen Mary University of London, School of Business and Management, Mile End Road, E1 4NS London, UK |
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Abstract: | In recent years, researchers have investigated a growing number of weighted networks where ties are differentiated according to their strength or capacity. Yet, most network measures do not take weights into consideration, and thus do not fully capture the richness of the information contained in the data. In this paper, we focus on a measure originally defined for unweighted networks: the global clustering coefficient. We propose a generalization of this coefficient that retains the information encoded in the weights of ties. We then undertake a comparative assessment by applying the standard and generalized coefficients to a number of network datasets. |
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Keywords: | Clustering Transitivity Weighted networks |
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