Genetic Algorithm with a Novel Leiden-based Mutation Operator for Community Detection.

Detecting quality community structures in complex networks is an important and highly active research area. Plenty of methods have been proposed for community detection in recent years. Among them, Genetic Algorithms (GAs) have been widely explored for community detection due to their strong competence at exploring the global discrete search space. However, existing GA algorithms for community detection still face major challenges when handling large and complex networks due to their use of random mutation operators. Whenever any candidate community structure in a GA population is mutated, a mutated node of the network under processing is often associated to a community with loose connections, seriously hurting GA's effectiveness and scalability. To address this issue, a newly designed Leiden-based GA (LGA) with a novel mutation operator based on the Leiden algorithm is proposed in this paper to improve the effectiveness of the mutation operator and the performance of the GA approach. Experiment results clearly show that LGA can achieve highly competitive performance in comparison to several state-of-the-art GA and non-GA community detection algorithms on multiple synthetic and real-world networks.