Abstract: Community detection is a technique used to reveal network clustering behaviors,capable of accurately identifying the community structure within a network,thus helping to better understand the internal organization and functions of complex networks.However,with the rapid development of these algorithms,concerns have arisen regarding issues such as information leakage and privacy invasion.In response,community hiding algorithms have been widely studied,which reduce the effectiveness of community detection algorithm and realize privacy protection by constructing perturbed substructure to blur the community structure in the network.Among the current methods for optimizing perturbation substructures,genetic algorithm-based approaches performs better.However,these methods often lack guidance directional in the search for solutions,leaving room for improvement in both the effectiveness and efficiency of constructing perturbation substructures.By incorporating gradient-guided information into the genetic algorithm search,the construction process of perturbation substructures can be optimized,enhancing the effectiveness and efficiency of community hiding.Experimental results demonstrate that integrating gradient-guided information into genetic algorithm search for perturbation substructures significantly outperforms other baseline methods for community hiding,proving its effectiveness.

Key words: Community detection, Community hiding, Gradient optimization, Evolutionary algorithm, Perturbed substructure