An Approach for Addressing Internally-Disconnected Communities in Louvain Algorithm

Fuente: arXiv
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Auteur principal: Sahu, Subhajit
Format: Preprint
Publié: 2024
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author Sahu, Subhajit
author_facet Sahu, Subhajit
contents Community detection is the problem of identifying densely connected clusters within a network. While the Louvain algorithm is commonly used for this task, it can produce internally-disconnected communities. To address this, the Leiden algorithm was introduced. This technical report introduces GSP-Louvain, a parallel algorithm based on Louvain, which mitigates this issue. Running on a system with two 16-core Intel Xeon Gold 6226R processors, GSP-Louvain outperforms Leiden, NetworKit Leiden, and cuGraph Leiden by 391x, 6.9x, and 2.6x respectively, processing 410M edges per second on a 3.8B edge graph. Furthermore, GSP-Louvain improves performance at a rate of 1.5x for every doubling of threads.
format Preprint
id arxiv_https___arxiv_org_abs_2402_11454
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle An Approach for Addressing Internally-Disconnected Communities in Louvain Algorithm
Sahu, Subhajit
Distributed, Parallel, and Cluster Computing
Social and Information Networks
G.2.2; I.5.3
Community detection is the problem of identifying densely connected clusters within a network. While the Louvain algorithm is commonly used for this task, it can produce internally-disconnected communities. To address this, the Leiden algorithm was introduced. This technical report introduces GSP-Louvain, a parallel algorithm based on Louvain, which mitigates this issue. Running on a system with two 16-core Intel Xeon Gold 6226R processors, GSP-Louvain outperforms Leiden, NetworKit Leiden, and cuGraph Leiden by 391x, 6.9x, and 2.6x respectively, processing 410M edges per second on a 3.8B edge graph. Furthermore, GSP-Louvain improves performance at a rate of 1.5x for every doubling of threads.
title An Approach for Addressing Internally-Disconnected Communities in Louvain Algorithm
topic Distributed, Parallel, and Cluster Computing
Social and Information Networks
G.2.2; I.5.3
url https://arxiv.org/abs/2402.11454