Affiliation:
1. School of Computer Science and Technology, Dongguan University of Technology, Dongguan 523808, China
Abstract
Community detection is a crucial research direction in the analysis of complex networks and has been shown to be an NP-hard problem (a problem that is at least as hard as the hardest problems in nondeterministic polynomial time). Multi-objective evolutionary algorithms (MOEAs) have demonstrated promising performance in community detection. Given that distinct crossover operators are suitable for various stages of algorithm evolution, we propose a two-stage algorithm that uses an individual similarity parameter to divide the algorithm into two stages. We employ appropriate crossover operators for each stage to achieve optimal performance. Additionally, a repair operation is applied to boundary-independent nodes during the second phase of the algorithm, resulting in improved community partitioning results. We assessed the effectiveness of the algorithm by measuring its performance on a synthetic network and four real-world network datasets. Compared to four existing competing methods, our algorithm achieves better accuracy and stability.
Funder
Ministry of Science and Technology of China
the Key Projects of Artificial Intelligence of High School in Guangdong Province
Dongguan Social Development Science and Technology Project
Dongguan Science and Technology Special Commissioner Project
Subject
General Mathematics,Engineering (miscellaneous),Computer Science (miscellaneous)
Cited by
4 articles.
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