English

Graph Clustering using Effective Resistance

Data Structures and Algorithms 2017-11-20 v1

Abstract

\def\vecc#1{\boldsymbol{#1}} We design a polynomial time algorithm that for any weighted undirected graph G=(V,E,\veccw)G = (V, E,\vecc w) and sufficiently large δ>1\delta > 1, partitions VV into subsets V1,,VhV_1, \ldots, V_h for some h1h\geq 1, such that \bullet at most δ1\delta^{-1} fraction of the weights are between clusters, i.e. w(Ei=1hE(Vi))w(E)δ; w(E - \cup_{i = 1}^h E(V_i)) \lesssim \frac{w(E)}{\delta}; \bullet the effective resistance diameter of each of the induced subgraphs G[Vi]G[V_i] is at most δ3\delta^3 times the average weighted degree, i.e. maxu,vViReffG[Vi](u,v)δ3Vw(E) for all i=1,,h. \max_{u, v \in V_i} \mathsf{Reff}_{G[V_i]}(u, v) \lesssim \delta^3 \cdot \frac{|V|}{w(E)} \quad \text{ for all } i=1, \ldots, h. In particular, it is possible to remove one percent of weight of edges of any given graph such that each of the resulting connected components has effective resistance diameter at most the inverse of the average weighted degree. Our proof is based on a new connection between effective resistance and low conductance sets. We show that if the effective resistance between two vertices uu and vv is large, then there must be a low conductance cut separating uu from vv. This implies that very mildly expanding graphs have constant effective resistance diameter. We believe that this connection could be of independent interest in algorithm design.

Keywords

Cite

@article{arxiv.1711.06530,
  title  = {Graph Clustering using Effective Resistance},
  author = {Vedat Levi Alev and Nima Anari and Lap Chi Lau and Shayan Oveis Gharan},
  journal= {arXiv preprint arXiv:1711.06530},
  year   = {2017}
}
R2 v1 2026-06-22T22:49:21.335Z