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• W2224335401 startingPage "023401" @default.
• W2224335401 abstract "Two concepts of centrality have been defined in complex networks. The first considers the centrality of a node and many different metrics for it has been defined (e.g. eigenvector centrality, PageRank, non-backtracking centrality, etc). The second is related to a large scale organization of the network, the core-periphery structure, composed by a dense core plus an outlying and loosely-connected periphery. In this paper we investigate the relation between these two concepts. We consider networks generated via the Stochastic Block Model, or its degree corrected version, with a strong core-periphery structure and we investigate the centrality properties of the core nodes and the ability of several centrality metrics to identify them. We find that the three measures with the best performance are marginals obtained with belief propagation, PageRank, and degree centrality, while non-backtracking and eigenvector centrality (or MINRES}, showed to be equivalent to the latter in the large network limit) perform worse in the investigated networks." @default.
• W2224335401 created "2016-06-24" @default.
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• W2224335401 date "2016-02-24" @default.
• W2224335401 modified "2023-10-02" @default.
• W2224335401 title "Centrality metrics and localization in core-periphery networks" @default.
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• W2224335401 doi "https://doi.org/10.1088/1742-5468/2016/02/023401" @default.
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