Bi-Objective Influence Maximization with Consideration of Node Burden

Abstract

Influence maximization aims at finding a set of nodes with the maximum influence in a network. Apart from the influence, the burden of nodes is worth considering as an objective but has been neglected in the literature. Herein, the burden refers to the capacity of a node to serve its influenced nodes, e.g., the storage of a device in the device-to-device networks. In this paper, we propose a new bi-objective influence maximization problem that jointly maximizes the influence of seed nodes and minimizes the variance of the burden of seed nodes, called Influence Maximization with Burden. We provide intuitive and empirical evidence to demonstrate the feasibility and necessity of the proposed problem. Furthermore, we develop two algorithms to solve the proposed influence maximization problem. One is a fast suboptimal algorithm based on the reverse reachable sampling method. The other is a memetic algorithm with a novel meme operator to identify promising nodes. The results of the experiments on synthesis and real networks illustrate that the proposed algorithms perform more effectively and efficiently than other existing algorithms. We published our codes on GitHub: https://github.com/fmyzckj.