A Comparative Analysis of Community Detection Algorithms on Artificial Networks

17 Pages Posted: 22 Mar 2017

See all articles by Zhao Yang

Zhao Yang

University of Zurich

René Algesheimer

University of Zurich

Claudio Tessone

University of Zurich

Date Written: 2016

Abstract

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.

Suggested Citation

Yang, Zhao and Algesheimer, René and Tessone, Claudio, A Comparative Analysis of Community Detection Algorithms on Artificial Networks (2016). Available at SSRN: https://ssrn.com/abstract=2937843 or http://dx.doi.org/10.2139/ssrn.2937843

Zhao Yang

University of Zurich ( email )

Rämistrasse 71
Zürich, CH-8006
Switzerland

René Algesheimer (Contact Author)

University of Zurich ( email )

Department of Business Administration
Andreasstrasse 15
Zurich, 8050
Switzerland

HOME PAGE: http://www.market-research.uzh.ch

Claudio Tessone

University of Zurich ( email )

Rämistrasse 71
Zürich, CH-8006
Switzerland

Register to save articles to
your library

Register

Paper statistics

Downloads
12
Abstract Views
120
PlumX Metrics
!

Under construction: SSRN citations while be offline until July when we will launch a brand new and improved citations service, check here for more details.

For more information