A robust optimization model for influence maximization in social networks with heterogeneous nodes

Abstract

Influence maximization is the problem of trying to maximize the number of influenced nodes by selecting optimal seed nodes, given that influencing these nodes is costly. Due to the probabilistic nature of the problem, existing approaches deal with the concept of the expected number of nodes. In the current research, a scenario-based robust optimization approach is taken to finding the most influential nodes. The proposed robust optimization model maximizes the number of infected nodes in the last step of diffusion while minimizing the number of seed nodes. Nodes, however, are treated as heterogeneous with regard to their propensity to pass messages along; or as having varying activation thresholds. Experiments are performed on a real text-messaging social network. The model developed here significantly outperforms some of the well-known existing heuristic approaches which are proposed in previous works.

A new stochastic diffusion model for influence maximization in social networks

“…defined a framework for robust influence maximization and then designed an algorithm to discover seed nodes from different models and parameters. In addition, Kermani et al focused on the probabilistic nature of the influence maximization problem and presented an algorithm based on a scenario-based robust optimization method to identify the seed nodes 37 . Both methods are brilliant studies, however in this paper; we consider the time-varying nature of the network by modeling the network as a stochastic graph model.…”