TIFIM: A Two-stage Iterative Framework for Influence Maximization in Social Networks

Influence Maximization is an important problem in social networks, and its main goal is to select some most influential initial nodes (i.e., seed nodes) to obtain the maximal influence spread. The existing studies primarily concentrate on the corresponding methods for influence maximization, including greedy algorithms, heuristic algorithms and their extensions to determine the most influential nodes. However, there is little work to ensure efficiency and accuracy of the proposed schemes at the same time. In this paper, a Two-stage Iterative Framework for the Influence Maximization in social networks, (i.e., TIFIM) is proposed. In order to exclude less influential nodes and decrease the computation complexity of TIFIM, in the first stage, an iterative framework in descending order is proposed to select the candidate nodes. In particular, based on the results of the last iteration and the two-hop measure, the First-Last Allocating Strategy (FLAS) is presented to compute the spread benefit of each node. We prove that TIFIM converges to a stable order within the finite iterations. In the second stage, we define the apical dominance to calculate the overlapping phenomenon of spread benefit among nodes and further propose Removal of the Apical Dominance (RAD) to determine seed nodes from the candidate nodes. Moreover, we also prove that the influence spread of TIFIM according to RAD converges to a specific value within finite computations. Finally, simulation results show that the proposed scheme has superior influence spread and running time than other existing ones. (C) 2019 Elsevier Inc. All rights reserved.