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# Graph Clustering using Effective Resistance

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LIPIcs.ITCS.2018.41.pdf
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## Cite As

Vedat Levi Alev, Nima Anari, Lap Chi Lau, and Shayan Oveis Gharan. Graph Clustering using Effective Resistance. In 9th Innovations in Theoretical Computer Science Conference (ITCS 2018). Leibniz International Proceedings in Informatics (LIPIcs), Volume 94, pp. 41:1-41:16, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2018)
https://doi.org/10.4230/LIPIcs.ITCS.2018.41

## Abstract

We design a polynomial time algorithm that for any weighted undirected graph G = (V, E, w) and sufficiently large \delta > 1, partitions V into subsets V(1),..., V(h) for some h>= 1, such that at most \delta^{-1} fraction of the weights are between clusters, i.e. sum(i < j) |E(V(i), V(j)| < w(E)/\delta and the effective resistance diameter of each of the induced subgraphs G[V(i)] is at most \delta^3 times the inverse of the average weighted degree, i.e. max{ Reff(u, v) : u, v \in V(i)} < \delta^3 · |V|/w(E) for all i = 1,..., 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 u and v is large, then there must be a low conductance cut separating u from v. 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
• Electrical Flows
• Effective Resistance
• Conductance
• Graph Partitioning

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