Robust Community Detection in Graphs

Community detection in network-type data provides a powerful tool in analyzing and understanding real-world systems. In fact, community detection approaches aim to reduce the network’s dimensionality and partition it into a set of disjoint clusters or communities. However, real networks are usually...
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Autor*in:	Esraa M. Al-Sharoa [verfasserIn] Bara' M. Ababneh [verfasserIn] Mahmood A. Alkhassaweneh [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2021

Schlagwörter:	Community detection graph theory robust principal component analysis symmetric nonnegative matrix factorization

Übergeordnetes Werk:	In: IEEE Access - IEEE, 2014, 9(2021), Seite 118757-118770
Übergeordnetes Werk:	volume:9 ; year:2021 ; pages:118757-118770

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1109/ACCESS.2021.3105692

Katalog-ID:	DOAJ004988442

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language	English
source	In IEEE Access 9(2021), Seite 118757-118770 volume:9 year:2021 pages:118757-118770
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institution	findex.gbv.de
topic_facet	Community detection graph theory robust principal component analysis symmetric nonnegative matrix factorization Electrical engineering. Electronics. Nuclear engineering
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authorswithroles_txt_mv	Esraa M. Al-Sharoa @@aut@@ Bara' M. Ababneh @@aut@@ Mahmood A. Alkhassaweneh @@aut@@
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callnumber-first	T - Technology
author	Esraa M. Al-Sharoa
spellingShingle	Esraa M. Al-Sharoa misc TK1-9971 misc Community detection misc graph theory misc robust principal component analysis misc symmetric nonnegative matrix factorization misc Electrical engineering. Electronics. Nuclear engineering Robust Community Detection in Graphs
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topic_title	TK1-9971 Robust Community Detection in Graphs Community detection graph theory robust principal component analysis symmetric nonnegative matrix factorization
topic	misc TK1-9971 misc Community detection misc graph theory misc robust principal component analysis misc symmetric nonnegative matrix factorization misc Electrical engineering. Electronics. Nuclear engineering
topic_unstemmed	misc TK1-9971 misc Community detection misc graph theory misc robust principal component analysis misc symmetric nonnegative matrix factorization misc Electrical engineering. Electronics. Nuclear engineering
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title	Robust Community Detection in Graphs
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abstract	Community detection in network-type data provides a powerful tool in analyzing and understanding real-world systems. In fact, community detection approaches aim to reduce the network’s dimensionality and partition it into a set of disjoint clusters or communities. However, real networks are usually corrupted with noise or outliers which affect the detected community structure quality. In this paper, a new robust community detection algorithm that is capable of recovering a clean or a smoothed version of the corrupted graph and detecting the correct community structure is introduced. The proposed approach combines robust principal component analysis (RPCA) and symmetric nonnegative matrix factorization (SymNMF) in a single optimization problem. The proposed problem is solved under the framework of alternating direction methods of multipliers (ADMM). In particular, the corrupted adjacency matrix is decomposed into a low-rank and sparse components using RPCA and the community structure is detected by applying SymNMF to the extracted low-rank component. Extensive experiments that have been conducted on real and simulated binary and weighted networks show that the proposed approach significantly outperforms existing algorithms in detecting the correct community structure even in grossly corrupted networks.
abstractGer	Community detection in network-type data provides a powerful tool in analyzing and understanding real-world systems. In fact, community detection approaches aim to reduce the network’s dimensionality and partition it into a set of disjoint clusters or communities. However, real networks are usually corrupted with noise or outliers which affect the detected community structure quality. In this paper, a new robust community detection algorithm that is capable of recovering a clean or a smoothed version of the corrupted graph and detecting the correct community structure is introduced. The proposed approach combines robust principal component analysis (RPCA) and symmetric nonnegative matrix factorization (SymNMF) in a single optimization problem. The proposed problem is solved under the framework of alternating direction methods of multipliers (ADMM). In particular, the corrupted adjacency matrix is decomposed into a low-rank and sparse components using RPCA and the community structure is detected by applying SymNMF to the extracted low-rank component. Extensive experiments that have been conducted on real and simulated binary and weighted networks show that the proposed approach significantly outperforms existing algorithms in detecting the correct community structure even in grossly corrupted networks.
abstract_unstemmed	Community detection in network-type data provides a powerful tool in analyzing and understanding real-world systems. In fact, community detection approaches aim to reduce the network’s dimensionality and partition it into a set of disjoint clusters or communities. However, real networks are usually corrupted with noise or outliers which affect the detected community structure quality. In this paper, a new robust community detection algorithm that is capable of recovering a clean or a smoothed version of the corrupted graph and detecting the correct community structure is introduced. The proposed approach combines robust principal component analysis (RPCA) and symmetric nonnegative matrix factorization (SymNMF) in a single optimization problem. The proposed problem is solved under the framework of alternating direction methods of multipliers (ADMM). In particular, the corrupted adjacency matrix is decomposed into a low-rank and sparse components using RPCA and the community structure is detected by applying SymNMF to the extracted low-rank component. Extensive experiments that have been conducted on real and simulated binary and weighted networks show that the proposed approach significantly outperforms existing algorithms in detecting the correct community structure even in grossly corrupted networks.
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title_short	Robust Community Detection in Graphs
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score	7.399584

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