Uncovering communities in complex networks using ant colony optimization

• Main Author: Chin, Yi Heng
• Format: Final Year Project / Dissertation / Thesis
• Published: 2023
• Subjects: QA Mathematics
• Online Access: http://eprints.utar.edu.my/6151/1/CHIN_YI_HENG_1903338.pdf; http://eprints.utar.edu.my/6151/

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.