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/lp/springer_journal/fast-detection-of-community-structures-using-graph-traversal-in-social-1rrt4t2byO
- 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
- D.O.I.
- 10.1007/s10115-018-1209-7
- Publisher site
- See Article on Publisher Site
Abstract
Knowl Inf Syst https://doi.org/10.1007/s10115-018-1209-7 REGULAR PAPER Fast detection of community structures using graph traversal in social networks 1 2 1 Partha Basuchowdhuri · Satyaki Sikdar · Varsha Nagarajan · 1 1 1 Khusbu Mishra · Surabhi Gupta · Subhashis Majumder Received: 19 March 2016 / Revised: 26 December 2017 / Accepted: 6 May 2018 © Springer-Verlag London Ltd., part of Springer Nature 2018 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, espe- cially 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 |) time to create an initial
Journal
Knowledge and Information Systems – Springer Journals
Published: May 31, 2018
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