Efficient influence spread estimation for influence maximization

Word-of-Mouth promotion is among the effective methods of marketing and is highly regarded by many commercial companies. This type of marketing is mapped on the influence maximization problem (IMP) in the social networks, and its goal is finding a specific set of the individuals with the maximum influence on the network. Therefore, in this paper, a heuristic-greedy algorithm named the HEDVGreedy algorithm was proposed for the IMP in the social networks. In this algorithm, the expected diffusion value of the graph nodes was calculated using the heuristic method, and then, the effective nodes were selected using the greedy method. Experimental results showed that the proposed algorithm has a high performance than the baseline algorithms while, it significantly reduces the running time of the computations under both the Independent Cascade and Weighted Cascade models in the eight real-world data sets.