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Influence maximization (IM) is a problem of selecting the most influential vertices with a limited budget under a given propagation model. A significant challenge faced by many existing algorithms pertains to their inability to reconcile the competing goals of solution quality and computational efficiency, rendering them unsuitable in large-scale social networks. In this paper, we propose an adaptive community-based influence maximization algorithm, named AComA, to solve the IM problem with a balance of effectiveness and efficiency. First, we introduce a community detection method to divide a large-scale network into several communities. An adaptive indicator is then defined to identify vertices with high propagation values in divided community networks. Based on community detection and the adaptive influence indicator, the number of candidate vertices is reduced, which significantly reduces the search space of …