We designed and developed of a new hybrid CPU+GPU parallel influence maximization algorithm (CuRipples) that is also capable of running on multi-GPU systems. Our approach uses techniques for efficiently sharing and scheduling of work between CPU and GPU, and data access and synchronization schemes to efficiently map the different steps of sampling and seed selection on a heterogeneous system. Our invention enables the use of state-of-the-art multi-GPU systems where our implementation is able to achieve drastic reductions in the time to solution, from hours to under a minute, while also significantly enhancing the quality of the solution.