Community-based influence maximization in social networks under a competitive linear threshold model.

The main purpose in influence maximization, which is motivated by the idea of viral marketing in social networks, is to find a subset of key users that maximize influence spread under a certain propagation model. A number of studies have been done over the past few years that try to solve this problem by considering a non-adversarial environment in which there exists only one player with no competitor. However, in real world scenarios, there is always more than one player competing with other players to influence the most nodes. This is called competitive influence maximization. Motivated by this, we try to solve the competitive influence maximization problem by proposing a new propagation model which is an extension of the Linear Threshold model and gives decision-making ability to nodes about incoming influence spread. We also propose an efficient algorithm for finding the influential nodes in a given social graph under the proposed propagation model which exploits the community structure of this graph to compute the spread of each node locally within its own community. The aim of our algorithm is to find the minimum number of seed nodes which can achieve higher spread in comparison with the spread achieved by nodes selected by other competitor. Our experiments on real world and synthetic datasets show that our approach can find influential nodes in an acceptable running time.