Local Algorithms for Estimating Effective Resistance

Peng, P., Lopatta, D., Yoshida, Y. and Goranci, G. (2021) Local Algorithms for Estimating Effective Resistance. In: 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD '21), Singapore, 14-18 Aug 2021, pp. 1329-1338. ISBN 9781450383325 (doi: 10.1145/3447548.3467361)

Abstract

Effective resistance is an important metric that measures the similarity of two vertices in a graph. It has found applications in graph clustering, recommendation systems and network reliability, among others. In spite of the importance of the effective resistances, we still lack efficient algorithms to exactly compute or approximate them on massive graphs. In this work, we design several local algorithms for estimating effective resistances, which are algorithms that only read a small portion of the input while still having provable performance guarantees. To illustrate, our main algorithm approximates the effective resistance between any vertex pair s,t with an arbitrarily small additive error ε in time O(poly (log n/ε)), whenever the underlying graph has bounded mixing time. We perform an extensive empirical study on several benchmark datasets, validating the performance of our algorithms.

• Item Type: Conference Proceedings
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Goranci, Dr Gramoz
• Authors: Peng, P., Lopatta, D., Yoshida, Y., and Goranci, G.
• College/School: College of Science and Engineering > School of Computing Science
• Journal Name: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining
• Publisher: ACM
• ISBN: 9781450383325
• Copyright Holders: Copyright © 2021 The Authors
• First Published: First published in Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD '21), pp 1329–1338
• Publisher Policy: Reproduced in accordance with the copyright policy of the publisher

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