  In this paper we present results from a method of community detection using
label propagation in undirected, unweighted graphs which incorporates elements
of neural computing and spike-based data. Using a fully connected,
edge-weighted system of spiking neurons driven by external currents, we
generate spike responses that are decoded into a binary signal. The similarity
between pairs of signals is quantified using a Hamming-distance based metric
and is used to classify vertices into communities. We test our approach on a
set of graph instances, each with 128 vertices and either homogeneous or
heterogeneous community size distributions. We present our method as a
candidate for a split-computing workflow that incorporates neuromorphic
hardware and does not require extensive pre-training of network parameters.
