English

Numerical Integration on Graphs: where to sample and how to weigh

Statistics Theory 2018-03-20 v1 Machine Learning Numerical Analysis Machine Learning Statistics Theory

Abstract

Let G=(V,E,w)G=(V,E,w) be a finite, connected graph with weighted edges. We are interested in the problem of finding a subset WVW \subset V of vertices and weights awa_w such that 1VvVf(v)wWawf(w) \frac{1}{|V|}\sum_{v \in V}^{}{f(v)} \sim \sum_{w \in W}{a_w f(w)} for functions f:VRf:V \rightarrow \mathbb{R} that are `smooth' with respect to the geometry of the graph. The main application are problems where ff is known to somehow depend on the underlying graph but is expensive to evaluate on even a single vertex. We prove an inequality showing that the integration problem can be rewritten as a geometric problem (`the optimal packing of heat balls'). We discuss how one would construct approximate solutions of the heat ball packing problem; numerical examples demonstrate the efficiency of the method.

Keywords

Cite

@article{arxiv.1803.06989,
  title  = {Numerical Integration on Graphs: where to sample and how to weigh},
  author = {George C. Linderman and Stefan Steinerberger},
  journal= {arXiv preprint arXiv:1803.06989},
  year   = {2018}
}
R2 v1 2026-06-23T00:57:44.783Z