The paper "Large-Scale Influence Maximization via Maximal Coverage Location" by E. Güney^a, M. Leitner^b, M. Ruthmair^c and M. Sinnl^d has been accepted for publication in European Journal of Operational Research. The journal is ranked A according to the VHB journal rankings.

^a MEF University, Department of Industrial Engineering, Turkey
^b Vrije Universiteit Amsterdam, Department of Supply Chain Analytics, Amsterdam, Netherlands
^c University of Vienna, Department of Statistics and Operations Research, Vienna, Austria
^d Johannes Kepler University Linz, Institute of Production and Logistics Management, Linz, Austria

Influence maximization aims at identifying a limited set of key individuals in a (social) network which spreads information based on some propagation model and maximizes the number of individuals reached. We show that influence maximization based on the probabilistic independent cascade model can be modeled as a stochastic maximal covering location problem. A reformulation based on Benders decomposition is proposed and a relation between obtained Benders optimality cuts and submodular cuts for correspondingly defined subsets is established. We introduce preprocessing tests, which allow us to remove variables from the model and develop efficient algorithms for the separation of Benders cuts. Both aspects are shown to be crucial ingredients of the developed branch-and-cut algorithm since real-life social network instances may be very large. In a computational study, the considered variants of this branch-and-cut algorithm outperform the state-of-the-art approach for influence maximization by orders of magnitude.