Abstract
We develop an algorithm to compute exact solutions to the influence maximization problem using concepts from reverse influence sampling (RIS). We implement the algorithm using GPU resources to evaluate the empirical accuracy of theoretically guaranteed greedy and RIS approximate solutions. We find that the approximations yield solutions that are remarkably close to optimal—usually achieving greater than 99% of the optimal influence spread. This accuracy is consistent across a wide range of network structures.
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