20.500.12556/DKUM-60192 Maximal neighbor similarity reveals real communities in networks An important problem in the analysis of network data is the detection of groups of densely interconnected nodes also called modules or communities. Community structure reveals functions and organizations of networks. Currently used algorithms for community detection in large-scale realworld networks are computationally expensive or require a priori information such as the number or sizes of communities or are not able to give the same resulting partition in multiple runs. In this paper we investigate a simple and fast algorithm that uses the network structure alone and requires neither optimization of pre-defined objective function nor information about number of communities. We propose a bottom up community detection algorithm in which starting from communities consisting of adjacent pairs of nodes and their maximal similar neighbors we find real communities. We show that the overall advantage of the proposed algorithm compared to the other community detection algorithms is its simple nature, low computational cost and its very high accuracy in detection communities of different sizes also in networks with blurred modularity structure consisting of poorly separated communities. All communities identified by the proposed method for facebook network and E-Coli transcriptional regulatory network have strong structural and functional coherence. networks real communities neighbor similarity omrežja realne skupnosti najbližji sosed true true true Nature Portfolio Angleški jezik Slovenski jezik Znanstveno delo 2016-06-09 03:16:10 2017-06-23 11:35:52 2025-09-17 11:37:54 2019-07-11 11:38:21 2015 0 0 Str. 1-10 št. članka 18374 Letn. 5 2015 0000-00-00 Zaloznikova Objavljeno NiDoloceno 2015-03-26 2015-11-16 2015-12-18 2045-2322 004.9 18727432 21987592 10.1038/srep18374 2045-2322 URN:SI:UM:DK:9U8R0H2S Scientific_Reports_2015_Zalik_Maximal_Neighbor_Similarity_Reveals_Real_Communities_in_Networks.pdf Scientific_Reports_2015_Zalik_Maximal_Neighbor_Similarity_Reveals_Real_Communities_in_Networks.pdf 1 2266642A441D3E7877F9613C047A3C47 a716344da8333aa2d81a4f64ae6aeb79147b261cecd2bbeb049cc03975d55d48 4a99de82-7c0e-11eb-bb7a-00155d0001ca https://dk.um.si/Dokument.php?lang=slv&id=113027 http://www.nature.com/articles/srep18374 1 https://dk.um.si/Dokument.php?lang=slv&id=94007 Fakulteta za naravoslovje in matematiko 0 0 0