# A low discrepancy sequence on graphs

arXiv: https://arxiv.org/abs/2010.04227 · HTML: https://mlsanity.com/paper/2010.04227/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Oct 2020.

## Built on by (most cited first, 1 of 1)

- [Scalable Graph Coreset Selection via Greedy Sampling](https://mlsanity.com/paper/2607.27602/) (2026)

## Abstract (arXiv)

Many applications such as election forecasting, environmental monitoring, health policy, and graph based machine learning require taking expectation of functions defined on the vertices of a graph. We describe a construction of a sampling scheme analogous to the so called Leja points in complex potential theory that can be proved to give low discrepancy estimates for the approximation of the expected value by the impirical expected value based on these points. In contrast to classical potential theory where the kernel is fixed and the equilibrium distribution depends upon the kernel, we fix a probability distribution and construct a kernel (which represents the graph structure) for which the equilibrium distribution is the given probability distribution. Our estimates do not depend upon the size of the graph.