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Influence Maximization (IM) has been extensively studied in network science,which attempts to find a subset of users to maximize the influence spread. Anew variant of IM, Fair Influence Maximization (FIM), which primarily enhancesthe fair propagation of information, attracts increasing attention in academic.However, existing algorithms for FIM suffer from a trade-off between fairnessand running time. Since it is a tough task to ensure that users are fairlyinfluenced in terms of sensitive attributes, such as race or gender, whilemaintaining a high influence spread. To tackle this problem, in this paper, wepropose an effective and efficient Community-based Evolutionary Algorithm forFIM (named CEA-FIM). In CEA-FIM, a community-based node selection strategy isproposed to identify potential nodes, which not only considers the size of thecommunity but also the attributes of the nodes in the community. Subsequently,we design an evolutionary algorithm based on the proposed node selectionstrategy to hasten the search for the optimal solution, including the novelinitialization, crossover and mutation strategies. We validate the proposedalgorithm CEA-FIM by performing experiments on real-world and syntheticnetworks. The experimental results show that the proposed CEA-FIM achieves abetter balance between effectiveness and efficiency, compared to thestate-of-the-art baseline algorithms.

Fair Influence Maximization in Social Networks: A Community-Based Evolutionary Algorithm