A K-shell Decomposition Based Heuristic Algorithm for Influence Maximization in Social Networks

Abstract

Influence maximization is a widely studied problem that aims to find a set of the most influential nodes to maximize the spread of influence as much as possible. A well-known method K-shell decomposition has a low time complexity, which can be able to quickly find the core nodes in the network who are regarded as the most influential spreaders. Motivated by this, we try to solve the effectiveness and the efficiency problem of influence maximization by utilizing the K-shell decomposition method, and propose a K-shell decomposition based heuristic algorithm called KDBH for finding the most influential nodes. We also design a novel assignment strategy of seeds to effectively avoid producing similar spreading areas by K-shell decomposition method. Furthermore, we take the direct influence and the indirect influence of nodes into account to further optimize the accuracy of seeds selection. At last, we conduct extensive experiments on real-world networks demonstrate that our algorithm performs better than other related algorithms.