Modularity-based model selection for kernel spectral clustering

A proper way of choosing the tuning parameters in a kernel model has a fundamental importance in determining the success of the model for a particular task. This paper is related to model selection in the framework of community detection on weighted and unweighted networks by means of a kernel spectral clustering model. Here we propose a new method based on Modularity (a popular measure of community structure in a network) which can deal with quite general situations (i.e. overlapping communities with different sizes). Thus we use Modularity criterion for model selection and not at the training level, which is the case of all the clustering algorithms proposed so far in the literature.