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Contribution Details

Type Journal Article
Scope Discipline-based scholarship
Title A Comparative Analysis of Community Detection Algorithms on Artificial Networks
Organization Unit
Authors
  • Zhao Yang
  • René Algesheimer
  • Claudio Tessone
Item Subtype Original Work
Refereed Yes
Status Published in final form
Language
  • English
Journal Title Scientific Reports
Publisher Nature Publishing Group
Geographical Reach international
ISSN 2045-2322
Volume 6
Page Range 30750
Date 2016
Abstract Text Many community detection algorithms have been developed to uncover the mesoscopic properties of complex networks. However how good an algorithm is, in terms of accuracy and computing time, remains still open. Testing algorithms on real-world network has certain restrictions which made their insights potentially biased: the networks are usually small, and the underlying communities are not defined objectively. In this study, we employ the Lancichinetti-Fortunato-Radicchi benchmark graph to test eight state-of-the-art algorithms. We quantify the accuracy using complementary measures and algorithms' computing time. Based on simple network properties and the aforementioned results, we provide guidelines that help to choose the most adequate community detection algorithm for a given network. Moreover, these rules allow uncovering limitations in the use of specific algorithms given macroscopic network properties. Our contribution is threefold: firstly, we provide actual techniques to determine which is the most suited algorithm in most circumstances based on observable properties of the network under consideration. Secondly, we use the mixing parameter as an easily measurable indicator of finding the ranges of reliability of the different algorithms. Finally, we study the dependency with network size focusing on both the algorithm's predicting power and the effective computing time.
Free access at PubMed ID
Digital Object Identifier 10.1038/srep30750
PubMed ID 27476470
Other Identification Number merlin-id:14017
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