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We consider the problem of Influence Maximization (IM), the task of selecting k seed nodes in a social network such that the expected number of nodes influenced is maximized. We propose a community-aware divide-and-conquer framework that involves (i) learning the inherent community structure of the social network, (ii) generating candidate solutions by solving the influence maximization problem for each community, and (iii ) selecting the final set of seed nodes using a novel progressiv e budgeting scheme. Our experiments on real-world social networks show that the proposed framework outperforms the standard methods in terms of run-time and the heuristic methods in terms of influence. We also study the effect of the community structure on the performance of the proposed framework. Our experiments sho w that the community structures with higher modularity lead the proposed framework to perform better in terms of run-time an d influence.