Community Detection Based on Genetic Algorithm Using Local Structural Similarity

Community detection is an important research direction in complex network analysis that aims to detect the community structure in networks via clustering operation, which has an important application value and practical significance in mining potential network information. Genetic algorithm (GA) is commonly used in community detection to solve NP-hard problems caused by modularity optimization effectively. However, in terms of GA, the problems of global search performance and slow convergence still remain due to the low accuracy of initial population generation and the randomness of the mutation operator. Thus, a novel local structural similarity-based GA (LSSGA) is proposed in this study to address such problems. It mainly contains two innovations: First, a new generation strategy for initial population based on local structural similarity and roulette wheel selection is designed to improve the quality of initial individuals, at the same time, maintain the diversity of the initial population. Secondly, an effective mutation operator based on label propagation and local structural similarity is proposed, which can quickly achieve targeted and effective mutation. The performance of LSSGA has been verified in many detailed aspects such as the convergence of initialization strategy, the effectiveness of overcome falling into local optimum, and rationality of objective function. Experimental results of both synthetic and real-world networks demonstrate the effective performance of the proposed method via systematic comparison with other existing advanced algorithms.