Kernel-based models for influence maximization on graphs based on Gaussian process variance minimization

The inference of novel knowledge, the discovery of hidden patterns, and the uncovering of insights from large amounts of data from a multitude of sources make data science to an art rather than just a mere scientific discipline. The study and design of mathematical models and signal processing tools able to analyze information represents a central research topic within data science. In this work, we introduce and investigate a model for influence maximization (IM) on graphs using ideas from kernel-based signal approximation, Gaussian process regression, and the minimization of a corresponding variance term. Data-driven approaches can be applied to determine proper kernels for this IM model and machine learning methodologies are adopted to tune the model parameters. Compared to stochastic models for influence maximization that rely on Monte-Carlo simulations, our kernel-based model allows for a simple and cost-efficient update strategy to compute optimal influencing nodes on a graph. In several numerical experiments, we show the properties and benefits of this model.