Decentralized dynamic understanding of hidden relations in complex networks

Abstract Almost all the natural or human made systems can be understood and controlled using complex networks. This is a difficult problem due to the very large number of elements in such networks, on the order of billions and higher, which makes it impossible to use conventional network analysis me...

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Autores principales:	Decebal Constantin Mocanu, Georgios Exarchakos, Antonio Liotta
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2018
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/4e65a67fd6834a63b6280afd5cb3a12f
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spelling	oai:doaj.org-article:4e65a67fd6834a63b6280afd5cb3a12f2021-12-02T15:07:56ZDecentralized dynamic understanding of hidden relations in complex networks10.1038/s41598-018-19356-42045-2322https://doaj.org/article/4e65a67fd6834a63b6280afd5cb3a12f2018-01-01T00:00:00Zhttps://doi.org/10.1038/s41598-018-19356-4https://doaj.org/toc/2045-2322Abstract Almost all the natural or human made systems can be understood and controlled using complex networks. This is a difficult problem due to the very large number of elements in such networks, on the order of billions and higher, which makes it impossible to use conventional network analysis methods. Herein, we employ artificial intelligence (specifically swarm computing), to compute centrality metrics in a completely decentralized fashion. More exactly, we show that by overlaying a homogeneous artificial system (inspired by swarm intelligence) over a complex network (which is a heterogeneous system), and playing a game in the fused system, the changes in the homogeneous system will reflect perfectly the complex network properties. Our method, dubbed Game of Thieves (GOT), computes the importance of all network elements (both nodes and edges) in polylogarithmic time with respect to the total number of nodes. Contrary, the state-of-the-art methods need at least a quadratic time. Moreover, the excellent capabilities of our proposed approach, it terms of speed, accuracy, and functionality, open the path for better ways of understanding and controlling complex networks.Decebal Constantin MocanuGeorgios ExarchakosAntonio LiottaNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 8, Iss 1, Pp 1-15 (2018)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Decebal Constantin Mocanu Georgios Exarchakos Antonio Liotta Decentralized dynamic understanding of hidden relations in complex networks
description	Abstract Almost all the natural or human made systems can be understood and controlled using complex networks. This is a difficult problem due to the very large number of elements in such networks, on the order of billions and higher, which makes it impossible to use conventional network analysis methods. Herein, we employ artificial intelligence (specifically swarm computing), to compute centrality metrics in a completely decentralized fashion. More exactly, we show that by overlaying a homogeneous artificial system (inspired by swarm intelligence) over a complex network (which is a heterogeneous system), and playing a game in the fused system, the changes in the homogeneous system will reflect perfectly the complex network properties. Our method, dubbed Game of Thieves (GOT), computes the importance of all network elements (both nodes and edges) in polylogarithmic time with respect to the total number of nodes. Contrary, the state-of-the-art methods need at least a quadratic time. Moreover, the excellent capabilities of our proposed approach, it terms of speed, accuracy, and functionality, open the path for better ways of understanding and controlling complex networks.
format	article
author	Decebal Constantin Mocanu Georgios Exarchakos Antonio Liotta
author_facet	Decebal Constantin Mocanu Georgios Exarchakos Antonio Liotta
author_sort	Decebal Constantin Mocanu
title	Decentralized dynamic understanding of hidden relations in complex networks
title_short	Decentralized dynamic understanding of hidden relations in complex networks
title_full	Decentralized dynamic understanding of hidden relations in complex networks
title_fullStr	Decentralized dynamic understanding of hidden relations in complex networks
title_full_unstemmed	Decentralized dynamic understanding of hidden relations in complex networks
title_sort	decentralized dynamic understanding of hidden relations in complex networks
publisher	Nature Portfolio
publishDate	2018
url	https://doaj.org/article/4e65a67fd6834a63b6280afd5cb3a12f
work_keys_str_mv	AT decebalconstantinmocanu decentralizeddynamicunderstandingofhiddenrelationsincomplexnetworks AT georgiosexarchakos decentralizeddynamicunderstandingofhiddenrelationsincomplexnetworks AT antonioliotta decentralizeddynamicunderstandingofhiddenrelationsincomplexnetworks
_version_	1718388313662947328

Decentralized dynamic understanding of hidden relations in complex networks

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