Uncovering communities in complex networks using ant colony optimization

Networks often refer to a set of connections between vertices with edges. A network is considered complex if it exhibits complex properties, such as a community structure. Recently, various community detection methods have been proposed by researchers to analyze complex networks. In this research, t...

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Main Author: Chin, Yi Heng
Format: Final Year Project / Dissertation / Thesis
Published: 2023
Subjects:
Online Access:http://eprints.utar.edu.my/6151/
http://eprints.utar.edu.my/6151/1/CHIN_YI_HENG_1903338.pdf
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author Chin, Yi Heng
author_facet Chin, Yi Heng
author_sort Chin, Yi Heng
building UTAR Institutional Repository
collection Online Access
description Networks often refer to a set of connections between vertices with edges. A network is considered complex if it exhibits complex properties, such as a community structure. Recently, various community detection methods have been proposed by researchers to analyze complex networks. In this research, the Ant Colony Optimization (ACO) algorithm is implemented by incorporating with the Label Propagation algorithm (LPA) to detect communities. The ACO algorithm forms the foundation for initial communities, which are then propagated to become the final communities using LPA. The ACO algorithm has also been extended to handle weighted and directed networks, allowing it to detect communities in such contexts. The performance of the proposed method will be evaluated using different benchmark networks, and the results will be compared with those obtained from existing community detection methods. Furthermore, the proposed method will be extended for implementation in real-world networks to detect communities.
first_indexed 2025-11-15T19:41:07Z
format Final Year Project / Dissertation / Thesis
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institution Universiti Tunku Abdul Rahman
institution_category Local University
last_indexed 2025-11-15T19:41:07Z
publishDate 2023
recordtype eprints
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spelling utar-61512023-12-12T08:30:47Z Uncovering communities in complex networks using ant colony optimization Chin, Yi Heng QA Mathematics Networks often refer to a set of connections between vertices with edges. A network is considered complex if it exhibits complex properties, such as a community structure. Recently, various community detection methods have been proposed by researchers to analyze complex networks. In this research, the Ant Colony Optimization (ACO) algorithm is implemented by incorporating with the Label Propagation algorithm (LPA) to detect communities. The ACO algorithm forms the foundation for initial communities, which are then propagated to become the final communities using LPA. The ACO algorithm has also been extended to handle weighted and directed networks, allowing it to detect communities in such contexts. The performance of the proposed method will be evaluated using different benchmark networks, and the results will be compared with those obtained from existing community detection methods. Furthermore, the proposed method will be extended for implementation in real-world networks to detect communities. 2023 Final Year Project / Dissertation / Thesis NonPeerReviewed application/pdf http://eprints.utar.edu.my/6151/1/CHIN_YI_HENG_1903338.pdf Chin, Yi Heng (2023) Uncovering communities in complex networks using ant colony optimization. Final Year Project, UTAR. http://eprints.utar.edu.my/6151/
spellingShingle QA Mathematics
Chin, Yi Heng
Uncovering communities in complex networks using ant colony optimization
title Uncovering communities in complex networks using ant colony optimization
title_full Uncovering communities in complex networks using ant colony optimization
title_fullStr Uncovering communities in complex networks using ant colony optimization
title_full_unstemmed Uncovering communities in complex networks using ant colony optimization
title_short Uncovering communities in complex networks using ant colony optimization
title_sort uncovering communities in complex networks using ant colony optimization
topic QA Mathematics
url http://eprints.utar.edu.my/6151/
http://eprints.utar.edu.my/6151/1/CHIN_YI_HENG_1903338.pdf