An ensemble learning strategy for graph clustering

This paper is on a graph clustering scheme inspired by ensemble learning. In short, the idea of ensemble learning is to learn several weak classifiers and use these weak classifiers to form a strong classifier. In this contribution, we use the generic procedure of ensemble learning and determine several weak graph clusterings (with respect to the objective function). From the partition given by the maximal overlap of these clusterings (the cluster cores), we continue the search for a strong clustering. We demonstrate the performance of this scheme by using it to maximize the modularity of a graph clustering. We show, that the quality of the initial weak clusterings is of minor importance for the quality of the final result of the scheme if we iterate the process of restarting from maximal overlaps. In addition to the empirical evaluation of the clustering scheme, we will link its search behavior to global analysis. With help of Morse theory and a discussion of the path space of the search heuristics we explain the superior search performance of this clustering scheme.