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The goal of influence maximization (IM) is to select a small set of seednodes which maximize the spread of influence on a network. In this work, wepropose BOPIM, a Bayesian Optimization (BO) algorithm for IM on temporalnetworks. The IM task is well-suited for a BO solution due to its expensive andcomplicated objective function. We propose a simple surrogate function to modelthe objective function and leverage Gaussian Process regression with shrinkagepriors to fit the model. An acquisition function based on the median of theposterior distribution leads to a straightforward procedure to select the nextsampling point. In numerical experiments on real-world networks, we find that,on average, the surrogate function estimates the true influence spread within afew nodes. Additionally, we show that BOPIM yields comparable influence spreadsto a gold-standard greedy algorithm while being as much as seventeen timesfaster. We also use these experiments to demonstrate the proposed method'sability to quantify uncertainty in optimal seed sets. To the knowledge of theauthor, this is the first attempt to look at uncertainty in the seed sets forIM, as well as the first application of BO to a constrained, combinatorialoptimization problem.

BOPIM: Bayesian Optimization for influence maximization on temporal networks