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Fast detection of community structures using graph traversal in social networks

Fast detection of community structures using graph traversal in social networks Finding community structures in social networks is considered to be a challenging task as many of the proposed algorithms are computationally expensive and does not scale well for large graphs. Most of the community detection algorithms proposed till date are unsuitable for applications that would require detection of communities in real time, especially for massive networks. The Louvain method, which uses modularity maximization to detect clusters, is usually considered to be one of the fastest community detection algorithms even without any provable bound on its running time. We propose a novel graph traversal-based community detection framework, which not only runs faster than the Louvain method but also generates clusters of better quality for most of the benchmark datasets. We show that our algorithms run in $$O(|V| + |E|)$$ O ( | V | + | E | ) time to create an initial cover before using modularity maximization to get the final cover. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Knowledge and Information Systems Springer Journals

Fast detection of community structures using graph traversal in social networks

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Publisher
Springer Journals
Copyright
Copyright © 2018 by Springer-Verlag London Ltd., part of Springer Nature
Subject
Computer Science; Information Systems and Communication Service; Database Management; Data Mining and Knowledge Discovery; Information Storage and Retrieval; Information Systems Applications (incl.Internet); IT in Business
ISSN
0219-1377
eISSN
0219-3116
DOI
10.1007/s10115-018-1209-7
Publisher site
See Article on Publisher Site

Abstract

Finding community structures in social networks is considered to be a challenging task as many of the proposed algorithms are computationally expensive and does not scale well for large graphs. Most of the community detection algorithms proposed till date are unsuitable for applications that would require detection of communities in real time, especially for massive networks. The Louvain method, which uses modularity maximization to detect clusters, is usually considered to be one of the fastest community detection algorithms even without any provable bound on its running time. We propose a novel graph traversal-based community detection framework, which not only runs faster than the Louvain method but also generates clusters of better quality for most of the benchmark datasets. We show that our algorithms run in $$O(|V| + |E|)$$ O ( | V | + | E | ) time to create an initial cover before using modularity maximization to get the final cover.

Journal

Knowledge and Information Systems – Springer Journals

Published: May 31, 2018

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