A Comparative Analysis of Community Detection Algorithms on Artificial Networks

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.

The ComDet Package - Iterated Community Detection for the Analysis of Community Membership, its Stability and the Detection of Fuzzy Community Boundaries.

“…However, spinglass community detection has already been validated on other kinds of networks, suggesting that the algorithm itself performs adequately (Yang et al, 2016).…”

“…Community Detection A network community can be defined as a subgraph of nodes which are more densely connected amongst each other than with nodes outside of the subgraph 1 (Yang, Algesheimer, & Tessone, 2016). Network science offers several ways to detect such communities in network graphs (Csárdi, 2017;Yang et al, 2016).…”

“…Network science offers several ways to detect such communities in network graphs (Csárdi, 2017;Yang et al, 2016). One such method is the spinglass community detection algorithm.…”

Hybrid Optimization Algorithm for Community and Fraud Detection in Complex Networks for High Immunity Towards Link and Node Failures

“…The determination of network community makes as to learn interaction among modules, prediction in unobserved connections, missing attribute values and inferring missing attribute values [7,8].…”

“…The networks are becoming wider and wider since it is the period of information explosion. Thus, we required many effective community detection algorithms for analyzing the networks with millions of vertices [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15].…”