English

Structure from Voltage

Machine Learning 2023-06-30 v2 Social and Information Networks

Abstract

Effective resistance (ER) is an attractive way to interrogate the structure of graphs. It is an alternative to computing the eigen-vectors of the graph Laplacian. Graph laplacians are used to find low dimensional structures in high dimensional data. Here too, ER based analysis has advantages over eign-vector based methods. Unfortunately Von Luxburg et al. (2010) show that, when vertices correspond to a sample from a distribution over a metric space, the limit of the ER between distant points converges to a trivial quantity that holds no information about the structure of the graph. We show that by using scaling resistances in a graph with nn vertices by n2n^2, one gets a meaningful limit of the voltages and of effective resistances. We also show that by adding a "ground" node to a metric graph one gets a simple and natural way to compute all of the distances from a chosen point to all other points.

Keywords

Cite

@article{arxiv.2203.00063,
  title  = {Structure from Voltage},
  author = {Robi Bhattacharjee and Alex Cloninger and Yoav Freund and Andreas Oslandsbotn},
  journal= {arXiv preprint arXiv:2203.00063},
  year   = {2023}
}
R2 v1 2026-06-24T09:56:59.386Z