Modeling node heterogeneity in latent space models for multidimensional networks

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Title: Modeling node heterogeneity in latent space models for multidimensional networks
Authors: D'Angelo, SilviaAlfò, MarcoMurphy, Thomas Brendan
Permanent link: http://hdl.handle.net/10197/11779
Date: Aug-2020
Online since: 2020-12-04T12:05:14Z
Abstract: Multidimensional network data can have different levels of complexity, as nodes may be characterized by heterogeneous individual‐specific features, which may vary across the networks. This article introduces a class of models for multidimensional network data, where different levels of heterogeneity within and between networks can be considered. The proposed framework is developed in the family of latent space models, and it aims to distinguish symmetric relations between the nodes and node‐specific features. Model parameters are estimated via a Markov Chain Monte Carlo algorithm. Simulated data and an application to a real example, on fruits import/export data, are used to illustrate and comment on the performance of the proposed models.
Funding Details: Science Foundation Ireland
Funding Details: Insight Research Centre
Type of material: Journal Article
Publisher: Wiley
Journal: Statistica Neerlandica
Volume: 74
Issue: 3
Start page: 324
End page: 341
Copyright (published version): 2020 the Authors, Statistica Neerlandica, VVS
Keywords: Latent space modelsMarkov chain Monte CarloMultiplex
DOI: 10.1111/stan.12209
Language: en
Status of Item: Peer reviewed
This item is made available under a Creative Commons License: https://creativecommons.org/licenses/by-nc-nd/3.0/ie/
Appears in Collections:Insight Research Collection

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