Influence maximization is the problem of finding a small set of seed nodes in a social network that maximizes the spread of influence under a certain diffusion model. The Greedy algorithm for influence maximization first proposed by Kempe, later improved by Leskovec suffers from two sources of computational deficiency: 1) the need to evaluate many candidate nodes before selecting a new seed in each round, and 2) the calculation of the influence spread of any seed set relies on Monte-Carlo simulations. In this work, we tackle both problems by devising efficient algorithms to compute influence spread and determine the best candidate for seed selection. The fundamental insight behind the proposed algorithms is the linkage between influence spread determination and belief propagation on a directed acyclic graph (DAG). Experiments using real-world social network graphs with scales ranging from thousands to millions of edges demonstrate the superior performance of the proposed algorithms with moderate computation costs.
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