Multiobjective evolutionary algorithm with graphlet measure for detection of complex topological and biological community structure

In the past few years, community structure detection has garnered much attention due to its significant role in analyzing complex network structures and functions. Detecting natural divisions in complex networks is proved to be an extremely Non-deterministic Polynomial-time hard (NP-hard) problem, w...

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Main Author:	Abduljabbar, Dhuha Abdulhadi
Format:	Thesis
Language:	English
Published:	2021
Subjects:	QA75 Electronic computers Computer science
Online Access:	http://eprints.utm.my/id/eprint/101493/1/DhuhaAbdulhadiAbduljabbarPSC2021.pdf.pdf
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spelling	my-utm-ep.1014932023-06-21T10:19:30Z Multiobjective evolutionary algorithm with graphlet measure for detection of complex topological and biological community structure 2021 Abduljabbar, Dhuha Abdulhadi QA75 Electronic computers. Computer science In the past few years, community structure detection has garnered much attention due to its significant role in analyzing complex network structures and functions. Detecting natural divisions in complex networks is proved to be an extremely Non-deterministic Polynomial-time hard (NP-hard) problem, which has been solved using evolutionary computation methods. Despite many efforts to design an effective community structure formula, the definition is still general, depending solely on the nodes' intra- and inter-connections. It lacks complete reflection of inherent topological properties, such as graphlet measure in terms of graphlet degree signatures and signature similarities, that can accurately detect complex communities' structure such as topological and biological community. The research proposes a new method termed MOEA_CGN (MultiObjective Evolutionary Algorithm based on Cooperation between Graphlet-based measure and Neighborhood relations) to improve the detection quality of complex topological and biological community structure in terms of accuracy and velocity. Thus, the contribution of this study is summarized in threefold. First, a new multiobjective optimization function is proposed to tackle the issue of a community structure definition. Second, a heuristic mutation operator is designed to enhance MOEA_CGN performance to accurately detect complex topological community structure by tackling the resolution limit problem. Third, the heuristic mutation operator is improved to make the MOEA_CGN method identify and detect complex biological community structure accurately by tackling the heterogeneity issue of real-world networks. Systematic experiments on different realworld networks from various domains and synthetic networks of different complexities have demonstrated the proposed method's effectiveness and robustness to define the community detection problem. Specifically, the proposed method has achieved detection reliability with an average improvement of 6.7% in detecting complex topological communities and 9.17% in detecting complex biological communities compared with the state-of-the-art benchmark studies. Moreover, the proposed MOEA_CGN method has demonstrated its ability to detect the optimal community structures in most complex networks faster than the competent multiobjective models based on community detection. 2021 Thesis http://eprints.utm.my/id/eprint/101493/ http://eprints.utm.my/id/eprint/101493/1/DhuhaAbdulhadiAbduljabbarPSC2021.pdf.pdf application/pdf en public http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:150558 phd doctoral Universiti Teknologi Malaysia Faculty of Engineering - School of Computing
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	QA75 Electronic computers Computer science
spellingShingle	QA75 Electronic computers Computer science Abduljabbar, Dhuha Abdulhadi Multiobjective evolutionary algorithm with graphlet measure for detection of complex topological and biological community structure
description	In the past few years, community structure detection has garnered much attention due to its significant role in analyzing complex network structures and functions. Detecting natural divisions in complex networks is proved to be an extremely Non-deterministic Polynomial-time hard (NP-hard) problem, which has been solved using evolutionary computation methods. Despite many efforts to design an effective community structure formula, the definition is still general, depending solely on the nodes' intra- and inter-connections. It lacks complete reflection of inherent topological properties, such as graphlet measure in terms of graphlet degree signatures and signature similarities, that can accurately detect complex communities' structure such as topological and biological community. The research proposes a new method termed MOEA_CGN (MultiObjective Evolutionary Algorithm based on Cooperation between Graphlet-based measure and Neighborhood relations) to improve the detection quality of complex topological and biological community structure in terms of accuracy and velocity. Thus, the contribution of this study is summarized in threefold. First, a new multiobjective optimization function is proposed to tackle the issue of a community structure definition. Second, a heuristic mutation operator is designed to enhance MOEA_CGN performance to accurately detect complex topological community structure by tackling the resolution limit problem. Third, the heuristic mutation operator is improved to make the MOEA_CGN method identify and detect complex biological community structure accurately by tackling the heterogeneity issue of real-world networks. Systematic experiments on different realworld networks from various domains and synthetic networks of different complexities have demonstrated the proposed method's effectiveness and robustness to define the community detection problem. Specifically, the proposed method has achieved detection reliability with an average improvement of 6.7% in detecting complex topological communities and 9.17% in detecting complex biological communities compared with the state-of-the-art benchmark studies. Moreover, the proposed MOEA_CGN method has demonstrated its ability to detect the optimal community structures in most complex networks faster than the competent multiobjective models based on community detection.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Abduljabbar, Dhuha Abdulhadi
author_facet	Abduljabbar, Dhuha Abdulhadi
author_sort	Abduljabbar, Dhuha Abdulhadi
title	Multiobjective evolutionary algorithm with graphlet measure for detection of complex topological and biological community structure
title_short	Multiobjective evolutionary algorithm with graphlet measure for detection of complex topological and biological community structure
title_full	Multiobjective evolutionary algorithm with graphlet measure for detection of complex topological and biological community structure
title_fullStr	Multiobjective evolutionary algorithm with graphlet measure for detection of complex topological and biological community structure
title_full_unstemmed	Multiobjective evolutionary algorithm with graphlet measure for detection of complex topological and biological community structure
title_sort	multiobjective evolutionary algorithm with graphlet measure for detection of complex topological and biological community structure
granting_institution	Universiti Teknologi Malaysia
granting_department	Faculty of Engineering - School of Computing
publishDate	2021
url	http://eprints.utm.my/id/eprint/101493/1/DhuhaAbdulhadiAbduljabbarPSC2021.pdf.pdf
_version_	1776100711093764096

Multiobjective evolutionary algorithm with graphlet measure for detection of complex topological and biological community structure

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