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

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