An Influence Maximization Algorithm Based on the Influence Propagation Range of Nodes

Abstract

The problem of influence maximization in the social network G is to find k seed nodes with the maximum influence. The seed set S has a wider range of influence in the social network G than other same-size node sets. The influence of a node is usually established by using the IC model (Independent Cascade model) with a considerable amount of Monte Carlo simulations used to approximate the influence of the node. In addition, an approximate effect (1 − 1/e) is obtained, when the number of Monte Carlo simulations is 10000 and the probability of propagation is very small. In this paper, we analyze that the propagative range of influence of node set is limited in the IC model, and we find that the influence of node only spread to the t′-th neighbor. Therefore, we propose a greedy algorithm based on the improved IC model that we only consider the influence in the t′-th neighbor of node. Finally, we perform experiments on 10 real social network and achieve favorable results.