Unsupervised Multi-population Evolutionary Algorithm for Community Detection in Attributed Networks

Community detection on attributed networks is a method to discover community structures within attributed networks. By applying community detection on attribute networks, we can better understand the relationships between nodes in real-world networks. However, current algorithms for community detection on attribute networks rely on hyper-parameters, and it is difficult to obtain an ideal result when facing networks with inconsistent attributes and topology. Consequently, we propose an Unsupervised Multi-population Evolutionary Algorithm (UMEA) for community detection in attributed networks. This algorithm adds edges between nodes based on attribute similarity, allowing it to combine attribute information during the process of community detection. In addition, this algorithm determines the optimal number of added edges autonomously through communication and learning between multiple populations. Furthermore, we propose a series of strategies to accelerate population convergence for the locus-based encoding. Experiments have demonstrated that our algorithm outperforms the benchmark algorithms in both real and artificial networks.