  Heterogeneity characterises real-world networks, where nodes show a broad
range of different topological features. However, nodes also tend to organise
into communities -- subsets of nodes that are sparsely inter-connected but are
densely intra-connected (more than the network's average connectivity). This
means that nodes belonging to the same community are close to each other by
some distance measure, such as the resistance distance, which is the effective
distance between any pair of nodes considering all possible paths. In this
work, we present automation (i.e., unsupervised) and missing accuracy tests for
a recently proposed semi-supervised community detection algorithm based on the
resistance distance. The accuracy testing involves quantifying our algorithm's
performance in terms of recovering known synthetic communities from benchmark
networks, where we present results for Girvan-Newman and
Lancichinetti-Fortunato-Radicchi networks. Our findings show that our algorithm
falls into the class of accurate performers.
