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Article

Distance Entropy Cartography Characterises Centrality in Complex Networks

Fondazione Bruno Kessler, Via Sommarive 18, 38123 Povo, Italy
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Entropy 2018, 20(4), 268; https://doi.org/10.3390/e20040268
Received: 28 February 2018 / Revised: 4 April 2018 / Accepted: 5 April 2018 / Published: 11 April 2018
(This article belongs to the Special Issue Graph and Network Entropies)
We introduce distance entropy as a measure of homogeneity in the distribution of path lengths between a given node and its neighbours in a complex network. Distance entropy defines a new centrality measure whose properties are investigated for a variety of synthetic network models. By coupling distance entropy information with closeness centrality, we introduce a network cartography which allows one to reduce the degeneracy of ranking based on closeness alone. We apply this methodology to the empirical multiplex lexical network encoding the linguistic relationships known to English speaking toddlers. We show that the distance entropy cartography better predicts how children learn words compared to closeness centrality. Our results highlight the importance of distance entropy for gaining insights from distance patterns in complex networks. View Full-Text
Keywords: complex networks; network measures; entropy; closeness centrality; multiplex lexical networks complex networks; network measures; entropy; closeness centrality; multiplex lexical networks
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MDPI and ACS Style

Stella, M.; De Domenico, M. Distance Entropy Cartography Characterises Centrality in Complex Networks. Entropy 2018, 20, 268. https://doi.org/10.3390/e20040268

AMA Style

Stella M, De Domenico M. Distance Entropy Cartography Characterises Centrality in Complex Networks. Entropy. 2018; 20(4):268. https://doi.org/10.3390/e20040268

Chicago/Turabian Style

Stella, Massimo, and Manlio De Domenico. 2018. "Distance Entropy Cartography Characterises Centrality in Complex Networks" Entropy 20, no. 4: 268. https://doi.org/10.3390/e20040268

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