Statistical Science

The Geometry of Continuous Latent Space Models for Network Data

Anna L. Smith, Dena M. Asta, and Catherine A. Calder

Full-text: Open access

Abstract

We review the class of continuous latent space (statistical) models for network data, paying particular attention to the role of the geometry of the latent space. In these models, the presence/absence of network dyadic ties are assumed to be conditionally independent given the dyads’ unobserved positions in a latent space. In this way, these models provide a probabilistic framework for embedding network nodes in a continuous space equipped with a geometry that facilitates the description of dependence between random dyadic ties. Specifically, these models naturally capture homophilous tendencies and triadic clustering, among other common properties of observed networks. In addition to reviewing the literature on continuous latent space models from a geometric perspective, we highlight the important role the geometry of the latent space plays on properties of networks arising from these models via intuition and simulation. Finally, we discuss results from spectral graph theory that allow us to explore the role of the geometry of the latent space, independent of network size. We conclude with conjectures about how these results might be used to infer the appropriate latent space geometry from observed networks.

Article information

Source
Statist. Sci., Volume 34, Number 3 (2019), 428-453.

Dates
First available in Project Euclid: 11 October 2019

Permanent link to this document
https://projecteuclid.org/euclid.ss/1570780978

Digital Object Identifier
doi:10.1214/19-STS702

Keywords
Geometric curvature graph Laplacian latent variable network model

Citation

Smith, Anna L.; Asta, Dena M.; Calder, Catherine A. The Geometry of Continuous Latent Space Models for Network Data. Statist. Sci. 34 (2019), no. 3, 428--453. doi:10.1214/19-STS702. https://projecteuclid.org/euclid.ss/1570780978


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