INCIM: A community-based algorithm for influence maximization problem under the linear threshold model

In this paper we propose an efficient algorithm (which has an acceptable response time even for large graphs) for finding the influential nodes in the graph under linear threshold model. We exploit the community structure of graph to find the influential communities, and then find the influence of each node as a combination of its local and global influences. We compare our algorithm with the state-of-the-art methods for influence maximization problem and the results of our experiments on real world datasets show that our approach outperforms the other ones in the quality of outputted influential nodes while still has acceptable running time and memory usage for large graphs.