@inproceedings{9d416615367540ff8c61573a2656d4f4,
title = "Expander Hierarchies for Normalized Cuts on Graphs",
abstract = "Expander decompositions of graphs have significantly advanced the understanding of many classical graph problems and led to numerous fundamental theoretical results. However, their adoption in practice has been hindered due to their inherent intricacies and large hidden factors in their asymptotic running times. Here, we introduce the first practically efficient algorithm for computing expander decompositions and their hierarchies and demonstrate its effectiveness and utility by incorporating it as the core component in a novel solver for the normalized cut graph clustering objective. Our extensive experiments on a variety of large graphs show that our expander-based algorithm outperforms state-of-the-art solvers for normalized cut with respect to solution quality by a large margin on a variety of graph classes such as citation, e-mail, and social networks or web graphs while remaining competitive in running time.",
keywords = "expander decomposition, expander hierarchy, graph clustering, graph partitioning, normalized cut",
author = "Kathrin Hanauer and Monika Henzinger and Robin M{\"u}nk and Harald R{\"a}cke and Maximilian V{\"o}tsch",
note = "Publisher Copyright: {\textcopyright} 2024 Copyright held by the owner/author(s).; 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2024 ; Conference date: 25-08-2024 Through 29-08-2024",
year = "2024",
month = aug,
day = "25",
doi = "10.1145/3637528.3671978",
language = "English",
series = "Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining",
publisher = "Association for Computing Machinery",
pages = "1016--1027",
booktitle = "KDD 2024 - Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining",
}