# Learning Sparse Graphs with a Core-periphery Structure

arXiv: https://arxiv.org/abs/2110.04022 · HTML: https://mlsanity.com/paper/2110.04022/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 29 references that are in the map.
- First posted on arXiv 8 Oct 2021.

## Builds on (most cited first, 6 of 6)

- [Learning graphs from data: A signal representation perspective](https://mlsanity.com/paper/1806.00848/) (2018)
- [Identification of core-periphery structure in networks](https://mlsanity.com/paper/1409.4813/) (2014)
- [Product Graph Learning from Multi-domain Data with Sparsity and Rank Constraints](https://mlsanity.com/paper/2012.08090/) (2020)
- [Producing a Unified Graph Representation from Multiple Social Network Views](https://mlsanity.com/paper/1301.5809/) (2013)
- [The role of centrality for the identification of influential spreaders in complex networks](https://mlsanity.com/paper/1404.4528/) (2014)
- [Random Spatial Network Models with Core-Periphery Structure](https://mlsanity.com/paper/1808.06544/) (2018)

## Built on by (most cited first, 1 of 1)

- [Generative Models and Learning Algorithms for Core-Periphery Structured Graphs](https://mlsanity.com/paper/2210.01489/) (2022)

## Abstract (arXiv)

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.