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

• Main Authors: Mehrdad Agha Mohammad Ali Kermani, Reza Ghesmati, Mir Saman Pishvaee
• Format: Article
• Language: English
• Published: SpringerOpen 2021-08-01
• Series: Computational Social Networks
• Subjects: Social network; Influence maximization; Influential nodes; Scenario-based stochastic programming; Robust optimization
• Online Access: https://doi.org/10.1186/s40649-021-00096-x

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.