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Influence maximization is the problem of selecting top $k$ seed nodes in asocial network to maximize their influence coverage under certain influencediffusion models. In this paper, we propose a novel algorithm IRIE thatintegrates a new message passing based influence ranking (IR), and influenceestimation (IE) methods for influence maximization in both the independentcascade (IC) model and its extension IC-N that incorporates negative opinionpropagations. Through extensive experiments, we demonstrate that IRIE matchesthe influence coverage of other algorithms while scales much better than allother algorithms. Moreover IRIE is more robust and stable than other algorithmsboth in running time and memory usage for various density of networks andcascade size. It runs up to two orders of magnitude faster than otherstate-of-the-art algorithms such as PMIA for large networks with tens ofmillions of nodes and edges, while using only a fraction of memory comparingwith PMIA.

IRIE: Scalable and Robust Influence Maximization in Social Networks