# Kernel-Based Models for Influence Maximization on Graphs based on Gaussian Process Variance Minimization

arXiv: https://arxiv.org/abs/2103.01575 · HTML: https://mlsanity.com/paper/2103.01575/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 42 references that are in the map.
- First posted on arXiv 2 Mar 2021.

## Builds on (most cited first, 9 of 9)

- [Graph Signal Processing: Overview, Challenges and Applications](https://mlsanity.com/paper/1712.00468/) (2017)
- [Stationary signal processing on graphs](https://mlsanity.com/paper/1601.02522/) (2016)
- [Vertex-Frequency Analysis on Graphs](https://mlsanity.com/paper/1307.5708/) (2013)
- [Learning to Learn Kernels with Variational Random Features](https://mlsanity.com/paper/2006.06707/) (2020)
- [Implicit Kernel Learning](https://mlsanity.com/paper/1902.10214/) (2019)
- [Partition of Unity Methods for Signal Processing on Graphs](https://mlsanity.com/paper/2012.10636/) (2020)
- [Graph signal interpolation with Positive Definite Graph Basis Functions](https://mlsanity.com/paper/1912.02069/) (2019)
- [Shapes of Uncertainty in Spectral Graph Theory](https://mlsanity.com/paper/1909.10865/) (2019)
- [Semi-Supervised Learning on Graphs with Feature-Augmented Graph Basis Functions](https://mlsanity.com/paper/2003.07646/) (2020)

## Abstract (arXiv)

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.