Graph-based clustering of gamma-ray bursts

Aims. An attempt to classify gamma-ray bursts (GRBs) with a low level of supervision using the state-of-the-start approaches stemming from graph theory was undertaken. Methods. Graph-based classification methods, relying on different variants of the $k$-nearest neighbour graph, were applied to various GRB samples in the duration-hardness ratio parameter space to infer the optimal partitioning. Results. In most cases it is found that both two and three groups are feasible, with the outcome being more ambiguous with an increasing sample size. Conclusions. There is no clear indication of the presence of a third GRB class; however, such a possibility cannot be ruled out with the employed methodology. There are no hints at more than three classes though.