Modularity-driven kernel k-means for community detection

Abstract

The k-means algorithm is probably the most well-known and most popular clustering method in existence today. This work evaluates if a new, autonomous, kernel k-means approach for graph node clustering coupled with the modularity criterion can rival, e.g., the well-established Louvain method. We test the algorithm on social network datasets of various sizes and types. The new method estimates the optimal kernel or distance parameters as well as the natural number of clusters in the dataset based on modularity. Results indicate that this simple black-box algorithm manages to perform on par with the Louvain method given the same input.