Learning Sparse Graphs with a Core-periphery Structure
This addresses the challenge of graph learning in network analysis, but it is incremental as it builds on existing core-periphery concepts.
The paper tackles the problem of learning sparse graphs with a core-periphery structure from node attributes alone, proposing a generative model that jointly infers a sparse graph and nodal core scores, with results showing good agreement with existing methods that use graph input.
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.