Earthquake clustering is a relevant feature of seismic catalogs, both in
time and space. Several methodologies for earthquake cluster
identification have been proposed in the literature in order to
characterise clustering properties and to analyse background seismicity.
We consider two recent data-driven declustering techniques, one is based
on nearest-neighbor distance and the other on a stochastic point
process. These two methods use different underlying assumptions and lead
to different classifications of earthquakes into background events and
secondary events. We investigated the classification similarities by
exploiting graph representations of earthquake clusters and tools from
network analysis. We found that the two declustering algorithms produce
similar partitions of the earthquake catalog into background events and
earthquake clusters, but they may differ in the identified topological
structure of the clusters. Especially the clusters obtained from the
stochastic method have a deeper complexity than the clusters from the
nearest-neighbor method. All of these similarities and differences can
be robustly recognised and quantified by the outdegree centrality and
closeness centrality measures from network analysis.