  In this paper, we focus on learning sparse graphs with a core-periphery
structure. We propose a generative model for data associated with
core-periphery structured networks to model the dependence of node attributes
on core scores of the nodes of a graph through a latent graph structure. Using
the proposed model, we jointly infer a sparse graph and nodal core scores that
induce dense (sparse) connections in core (respectively, peripheral) parts of
the network. Numerical experiments on a variety of real-world data indicate
that the proposed method learns a core-periphery structured graph from node
attributes alone, while simultaneously learning core score assignments that
agree well with existing works that estimate core scores using graph as input
and ignoring commonly available node attributes.
