Community detection: Comparison among clustering algorithms and application to EEG-based brain networks

Community structure is a feature of complex networks that can be crucial for the understanding of their internal organization. This is particularly true for brain networks, as the brain functioning is thought to be based on a modular organization. In the last decades, many clustering algorithms were developed with the aim to identify communities in networks of different nature. However, there is still no agreement about which one is the most reliable, and to test and compare these algorithms under a variety of conditions would be beneficial to potential users. In this study, we performed a comparative analysis between six different clustering algorithms, analyzing their performances on a ground-truth consisting of simulated networks with properties spanning a wide range of conditions. Results show the effect of factors like the noise level, the number of clusters, the network dimension and density on the performances of the algorithms and provide some guidelines about the use of the more appropriate algorithm according to the different conditions. The best performances under a wide range of conditions were obtained by Louvain and Leicht & Newman algorithms, while Ronhovde and Infomap proved to be more appropriate in very noisy conditions. Finally, as a proof of concept, we applied the algorithms under exam to brain functional connectivity networks obtained from EEG signals recorded during a sustained movement of the right hand, obtaining a clustering of scalp electrodes which agrees with the results of the simulation study conducted.