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Title:Maximal neighbor similarity reveals real communities in networks
Authors:ID Rizman Žalik, Krista (Author)
Files:.pdf Scientific_Reports_2015_Zalik_Maximal_Neighbor_Similarity_Reveals_Real_Communities_in_Networks.pdf (2,21 MB)
MD5: 2266642A441D3E7877F9613C047A3C47
 
URL http://www.nature.com/articles/srep18374
 
Language:English
Work type:Scientific work
Typology:1.01 - Original Scientific Article
Organization:FNM - Faculty of Natural Sciences and Mathematics
Abstract: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.
Keywords:networks, real communities, neighbor similarity
Publication status:Published
Publication version:Version of Record
Submitted for review:26.03.2015
Article acceptance date:16.11.2015
Publication date:18.12.2015
Publisher:Nature Portfolio
Year of publishing:2015
Number of pages:Str. 1-10
Numbering:Letn. 5, št. članka 18374
PID:20.500.12556/DKUM-60192 New window
ISSN:2045-2322
UDC:004.9
ISSN on article:2045-2322
COBISS.SI-ID:21987592 New window
DOI:10.1038/srep18374 New window
NUK URN:URN:SI:UM:DK:9U8R0H2S
Publication date in DKUM:23.06.2017
Views:1323
Downloads:411
Metadata:XML DC-XML DC-RDF
Categories:Misc.
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Record is a part of a journal

Title:Scientific reports
Shortened title:Sci. rep.
Publisher:Nature Publishing Group
ISSN:2045-2322
COBISS.SI-ID:18727432 New window

Document is financed by a project

Funder:ARRS - Slovenian Research Agency
Project number:P2-0041
Name:Računalniški sistemi, metodologije in inteligentne storitve

Licences

License:CC BY 4.0, Creative Commons Attribution 4.0 International
Link:http://creativecommons.org/licenses/by/4.0/
Description:This is the standard Creative Commons license that gives others maximum freedom to do what they want with the work as long as they credit the author.
Licensing start date:09.06.2016

Secondary language

Language:Slovenian
Keywords:omrežja, realne skupnosti, najbližji sosed


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