  Influence maximization is the problem of selecting top $k$ seed nodes in a
social network to maximize their influence coverage under certain influence
diffusion models. In this paper, we propose a novel algorithm IRIE that
integrates a new message passing based influence ranking (IR), and influence
estimation (IE) methods for influence maximization in both the independent
cascade (IC) model and its extension IC-N that incorporates negative opinion
propagations. Through extensive experiments, we demonstrate that IRIE matches
the influence coverage of other algorithms while scales much better than all
other algorithms. Moreover IRIE is more robust and stable than other algorithms
both in running time and memory usage for various density of networks and
cascade size. It runs up to two orders of magnitude faster than other
state-of-the-art algorithms such as PMIA for large networks with tens of
millions of nodes and edges, while using only a fraction of memory comparing
with PMIA.
