  Influence maximization, the fundamental of viral marketing, aims to find
top-$K$ seed nodes maximizing influence spread under certain spreading models.
In this paper, we study influence maximization from a game perspective. We
propose a Coordination Game model, in which every individual makes its decision
based on the benefit of coordination with its network neighbors, to study
information propagation. Our model serves as the generalization of some
existing models, such as Majority Vote model and Linear Threshold model. Under
the generalized model, we study the hardness of influence maximization and the
approximation guarantee of the greedy algorithm. We also combine several
strategies to accelerate the algorithm. Experimental results show that after
the acceleration, our algorithm significantly outperforms other heuristics, and
it is three orders of magnitude faster than the original greedy method.
