Is normalized mutual information a fair measure for comparing community detection methods?

Abstract

Normalized mutual information (NMI) is a widely used measure to compare community detection methods. Recently, however, the need of adjustment for information theoretic based measures has been argued because of their tendency in choosing clustering solutions with more communities. In this paper an experimental evaluation is performed to investigate this problem, and an adjustment that scales the values of NMI is proposed. Experiments on synthetic generated networks highlight the unbiased behavior of scaled NMI.