Enhancing equity and influence spread in overlapping communities via multi-objective optimization and multi-transformation framework

Influence maximization in social networks aims to identify a set of seed nodes that can maximize the spread of information throughout the network. However, traditional influence maximization approaches primarily focus on maximizing overall influence spread while often neglecting fairness across communities, particularly in networks with overlapping community structures. This limitation can lead to biased diffusion and unequal information access among different social groups. To address this issue, this study introduces a framework for enhancing both equity and influence spread in overlapping communities through a multi-objective optimization perspective. Specifically, we formulate the problem as a Multi-Objective Fair Influence Maximization in Overlapping Communities (MOFIM-OC), which jointly optimizes influence diffusion performance and equity fairness across community structures. The proposed approach incorporates overlapping community characteristics to better capture complex social interactions and ensure fair consideration of both shared and non-shared nodes. In addition, a multi-transformation mechanism is introduced to enhance the search capability of the optimization process by enabling multiple representation spaces of the diffusion dynamics. To solve the proposed problem efficiently, we develop a unified multi-objective optimization framework that integrates evolutionary search strategies with transformation-based learning mechanisms. This hybrid design improves exploration capability, balances convergence and diversity, and enhances the quality of the obtained Pareto solutions. Extensive experiments on several real-world social networks demonstrate that the proposed approach consistently outperforms state-of-the-art baselines in terms of both influence spread and fairness metrics. The results further confirm that integrating overlapping community structures with multi-objective and multi-transformation optimization significantly improves diffusion equity without sacrificing overall performance.