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LEARNING SPARSE GRAPHS WITH A CORE-PERIPHERY STRUCTURE

Citation Author(s):
Submitted by:
Sravanthi Gurug...
Last updated:
10 May 2022 - 3:23am
Document Type:
Presentation Slides
Document Year:
2022
Event:
Paper Code:
4483

Abstract

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.

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