  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 (DS) to an art rather than just a mere scientific discipline.
The study and design of mathematical models able to analyze information
represents a central research topic in DS. In this work, we introduce and
investigate a novel model for influence maximization (IM) on graphs using ideas
from kernel-based 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 in this field that rely on costly Monte-Carlo simulations, our 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 new model.
