  Network homophily, the tendency of similar nodes to be connected, and
transitivity, the tendency of two nodes being connected if they share a common
neighbor, are conflated properties in network analysis, since one mechanism can
drive the other. Here we present a generative model and corresponding inference
procedure that are capable of distinguishing between both mechanisms. Our
approach is based on a variation of the stochastic block model (SBM) with the
addition of triadic closure edges, and its inference can identify the most
plausible mechanism responsible for the existence of every edge in the network,
in addition to the underlying community structure itself. We show how the
method can evade the detection of spurious communities caused solely by the
formation of triangles in the network, and how it can improve the performance
of edge prediction when compared to the pure version of the SBM without triadic
closure.
