# Generative Models and Learning Algorithms for Core-Periphery Structured Graphs

arXiv: https://arxiv.org/abs/2210.01489 · HTML: https://mlsanity.com/paper/2210.01489/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 25 references that are in the map.
- First posted on arXiv 4 Oct 2022.

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

- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [A Comprehensive Survey on Graph Neural Networks](https://mlsanity.com/paper/1901.00596/) (2019)
- [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)
- [Learning Sparse Graphs with a Core-periphery Structure](https://mlsanity.com/paper/2110.04022/) (2021)

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

- [Hierarchical graph sampling based minibatch learning with chain preservation and variance reduction](https://mlsanity.com/paper/2503.00860/) (2025)

## Abstract (arXiv)

We consider core-periphery structured graphs, which are graphs with a group of densely and sparsely connected nodes, respectively, referred to as core and periphery nodes. The so-called core score of a node is related to the likelihood of it being a core node. In this paper, we focus on learning the core scores of a graph from its node attributes and connectivity structure. To this end, we propose two classes of probabilistic graphical models: affine and nonlinear. First, we describe affine generative models to model the dependence of node attributes on its core scores, which determine the graph structure. Next, we discuss nonlinear generative models in which the partial correlations of node attributes influence the graph structure through latent core scores. We develop algorithms for inferring the model parameters and core scores of a graph when both the graph structure and node attributes are available. When only the node attributes of graphs are available, we jointly learn a core-periphery structured graph and its core scores. We provide results from numerical experiments on several synthetic and real-world datasets to demonstrate the efficacy of the developed models and algorithms.