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2022 Markov random geometric graph, MRGG: A growth model for temporal dynamic networks
Quentin Duchemin, Yohann De Castro
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Electron. J. Statist. 16(1): 671-699 (2022). DOI: 10.1214/21-EJS1969

Abstract

We introduce Markov Random Geometric Graphs (MRGGs), a growth model for temporal dynamic networks. It is based on a Markovian latent space dynamic: consecutive latent points are sampled on the Euclidean Sphere using an unknown Markov kernel; and two nodes are connected with a probability depending on a unknown function of their latent geodesic distance.

More precisely, at each stamp-time k we add a latent point Xk sampled by jumping from the previous one Xk1 in a direction chosen uniformly Yk and with a length rk drawn from an unknown distribution called the latitude function. The connection probabilities between each pair of nodes are equal to the envelope function of the distance between these two latent points. We provide theoretical guarantees for the non-parametric estimation of the latitude and the envelope functions.

We propose an efficient algorithm that achieves those non-parametric estimation tasks based on an ad-hoc Hierarchical Agglomerative Clustering approach. As a by product, we show how MRGGs can be used to detect dependence structure in growing graphs and to solve link prediction problems.

Funding Statement

This work was supported by a grant from Région Ile-de-France.

Citation

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Quentin Duchemin. Yohann De Castro. "Markov random geometric graph, MRGG: A growth model for temporal dynamic networks." Electron. J. Statist. 16 (1) 671 - 699, 2022. https://doi.org/10.1214/21-EJS1969

Information

Received: 1 May 2021; Published: 2022
First available in Project Euclid: 14 January 2022

Digital Object Identifier: 10.1214/21-EJS1969

Subjects:
Primary: 05C80
Secondary: 05C62 , 60J05 , 62G05

Keywords: link prediction , Markov chains , Non-parametric estimation , Random geometric graph , spectral methods

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Vol.16 • No. 1 • 2022
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