  Latent Euclidean embedding models a given network by representing each node
in a Euclidean space, where the probability of two nodes sharing an edge is a
function of the distances between the nodes. This implies that for two nodes to
share an edge with high probability, they must be relatively close in all
dimensions. This constraint may be overly restrictive for describing modern
networks, in which having similarities in at least one area may be sufficient
for having a high edge probability. We introduce a new model, which we call
Latent Channel Networks, which allows for such features of a network. We
present an EM algorithm for fitting the model, for which the computational
complexity is linear in the number of edges and number of channels and apply
the algorithm to both synthetic and classic network datasets.
