Maximal neighbor similarity reveals real communities in networks

Rizman Žalik, Krista

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Naslov:	Maximal neighbor similarity reveals real communities in networks
Avtorji:	ID Rizman Žalik, Krista (Avtor)
Datoteke:	Scientific_Reports_2015_Zalik_Maximal_Neighbor_Similarity_Reveals_Real_Communities_in_Networks.pdf (2,21 MB) MD5: 2266642A441D3E7877F9613C047A3C47 http://www.nature.com/articles/srep18374
Jezik:	Angleški jezik
Vrsta gradiva:	Znanstveno delo
Tipologija:	1.01 - Izvirni znanstveni članek
Organizacija:	FNM - Fakulteta za naravoslovje in matematiko
Opis:	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.
Ključne besede:	networks, real communities, neighbor similarity
Status publikacije:	Objavljeno
Verzija publikacije:	Objavljena publikacija
Poslano v recenzijo:	26.03.2015
Datum sprejetja članka:	16.11.2015
Datum objave:	18.12.2015
Založnik:	Nature Portfolio
Leto izida:	2015
Št. strani:	Str. 1-10
Številčenje:	Letn. 5, št. članka 18374
PID:	20.500.12556/DKUM-60192
ISSN:	2045-2322
UDK:	004.9
COBISS.SI-ID:	21987592
DOI:	10.1038/srep18374
ISSN pri članku:	2045-2322
NUK URN:	URN:SI:UM:DK:9U8R0H2S
Datum objave v DKUM:	23.06.2017
Število ogledov:	1321
Število prenosov:	411
Metapodatki:
Področja:	Ostalo
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Gradivo je del revije

Naslov:	Scientific reports
Skrajšan naslov:	Sci. rep.
Založnik:	Nature Publishing Group
ISSN:	2045-2322
COBISS.SI-ID:	18727432

Gradivo je financirano iz projekta

Financer:	ARRS - Agencija za raziskovalno dejavnost Republike Slovenije
Številka projekta:	P2-0041
Naslov:	Računalniški sistemi, metodologije in inteligentne storitve

Licence

Licenca:	CC BY 4.0, Creative Commons Priznanje avtorstva 4.0 Mednarodna

Povezava:	http://creativecommons.org/licenses/by/4.0/deed.sl
Opis:	To je standardna licenca Creative Commons, ki daje uporabnikom največ možnosti za nadaljnjo uporabo dela, pri čemer morajo navesti avtorja.
Začetek licenciranja:	09.06.2016

Sekundarni jezik

Jezik:	Slovenski jezik
Ključne besede:	omrežja, realne skupnosti, najbližji sosed

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