The idea underlying modal clustering is to associate groups with the regions around the modes of the probability density function underlying the data. This correspondence between clusters and dense regions in the sample space is here exploited to discuss a possible extension of modal clustering to the analysis of social networks. Such extension, albeit non trivial, seems particularly appealing: conceptually, the notion of high-density cluster fits well the one of cluster in a network, where groups are usually regarded as collections of individuals with dense local ties in their neighborhood. Additionally, modal clustering often resorts to graph theory for the operational detection of clusters, which is another condition that seems particularly appropriate to deal with relational data.

Modal clustering of social network

DE STEFANO, DOMENICO
2014

Abstract

The idea underlying modal clustering is to associate groups with the regions around the modes of the probability density function underlying the data. This correspondence between clusters and dense regions in the sample space is here exploited to discuss a possible extension of modal clustering to the analysis of social networks. Such extension, albeit non trivial, seems particularly appealing: conceptually, the notion of high-density cluster fits well the one of cluster in a network, where groups are usually regarded as collections of individuals with dense local ties in their neighborhood. Additionally, modal clustering often resorts to graph theory for the operational detection of clusters, which is another condition that seems particularly appropriate to deal with relational data.
978-88-8467-874-4
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11368/2903991
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