Dynamic Latent Space Model With Position Clusters and Its Application in International Trade Network

Jiajia Wang

Discrete Dynamics in Nature and Society, 2025, vol. 2025, 1-16

Abstract: The dynamic latent space model is widely used in analysing network data. It can provide useful visualization and interpretation of networks, as well as represent the inherent reciprocity and transitivity. In this paper, a dynamic latent space model with position clusters is proposed. The model extends the dynamic latent space model by incorporating latent position clustering and accounting for weighted networks. A fully Bayesian method with adaptive Markov chain Monte Carlo sampling is used to estimate the novel model. A purity-based relabelling algorithm is proposed to resolve label switching. This model can be extended to analyse binary networks, count networks and sparse weighted networks. Finally, the model is used to analyse the product trade data of 54 countries from 2010 to 2019.

Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://downloads.hindawi.com/journals/ddns/2025/6643841.pdf (application/pdf)
http://downloads.hindawi.com/journals/ddns/2025/6643841.xml (application/xml)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:hin:jnddns:6643841

DOI: 10.1155/ddns/6643841

Access Statistics for this article

More articles in Discrete Dynamics in Nature and Society from Hindawi
Bibliographic data for series maintained by Mohamed Abdelhakeem ().