paper/paper.md
In jongheepark/NetworkChange: Bayesian Package for Network Changepoint Analysis
title: 'NetworkChange: Analyzing Network Changes in R'
tags:
- R
- longitudinal networks
- latent space
- hidden Markov models
- Bayesian inference
authors:
- affiliation: 1
name: Jong Hee Park
orcid: 0000-0002-4598-3456
- affiliation: 2
name: Yunkyu Sohn
orcid: 0000-0002-9496-1907
affiliations:
- index: 1
name: Department of Political Science and International Relations, Seoul National University
- index: 2
name: Faculty of Political Science and Economics, Waseda University
date: 9 July 2020
bibliography: paper.bib
aas-doi: 10.3847/xxxxx
aas-journal: Journal of Open Source Software.
Summary
NetworkChange is an R package that detects multiple structural
changes in longitudinal network data using the latent space approach [@hoff2002latent].
Based on the Bayesian multi-array representation of longitudinal
networks [@Hoff2015], NetworkChange performs
Bayesian hidden Markov analysis to discover changes in structural
network features across temporal layers using the hidden Markov model formulation. NetworkChange can detect
various forms of changes in network structure such as block-splitting, block-merging,
and core-periphery changes. NetworkChange also provides functions
for model diagnostics using WAIC, average loss, and log marginal
likelihoods as well as visualization tools for dynamic analysis results
of longitudinal networks.
The package is designed for R users who need to analyze longitudinal network data to discover latent node-level characteristics including cases when there are discrete changes of the underlying states governing the node-level characteristics. This is in contrast to an R package for latent space and cluster analysis of networks [@krivitsky2008fitting] which does not incorporate a state space model (e.g. hidden Markov model) and a Python code for longitudinal network analysis [@peel2015detecting] under a distinct formulation (hierarchical random graph model) with a changepoint detection function. In addition to functions for the statistical analysis, NetworkChange provides visualization functions for summary of the analysis results (\autoref{fig:list}). The complete guide for using core functions of the package is presented at https://github.com/jongheepark/NetworkChange as its vignette with an empirical data set analysis example. @ParkSohn2020 provide methodological details of the algorithms implemented in the package.
Acknowledgements
This work was supported by the Japan Society for the Promotion of Science Early-Career Scientists Grant [JP19K13606] to Y.S.
References
jongheepark/NetworkChange documentation built on March 23, 2022, 4:40 a.m.
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title: 'NetworkChange: Analyzing Network Changes in R' tags: - R - longitudinal networks - latent space - hidden Markov models - Bayesian inference authors: - affiliation: 1 name: Jong Hee Park orcid: 0000-0002-4598-3456 - affiliation: 2 name: Yunkyu Sohn orcid: 0000-0002-9496-1907 affiliations: - index: 1 name: Department of Political Science and International Relations, Seoul National University - index: 2 name: Faculty of Political Science and Economics, Waseda University date: 9 July 2020 bibliography: paper.bib aas-doi: 10.3847/xxxxx aas-journal: Journal of Open Source Software.
Summary
NetworkChange is an R package that detects multiple structural changes in longitudinal network data using the latent space approach [@hoff2002latent]. Based on the Bayesian multi-array representation of longitudinal networks [@Hoff2015], NetworkChange performs Bayesian hidden Markov analysis to discover changes in structural network features across temporal layers using the hidden Markov model formulation. NetworkChange can detect various forms of changes in network structure such as block-splitting, block-merging, and core-periphery changes. NetworkChange also provides functions for model diagnostics using WAIC, average loss, and log marginal likelihoods as well as visualization tools for dynamic analysis results of longitudinal networks.
The package is designed for R users who need to analyze longitudinal network data to discover latent node-level characteristics including cases when there are discrete changes of the underlying states governing the node-level characteristics. This is in contrast to an R package for latent space and cluster analysis of networks [@krivitsky2008fitting] which does not incorporate a state space model (e.g. hidden Markov model) and a Python code for longitudinal network analysis [@peel2015detecting] under a distinct formulation (hierarchical random graph model) with a changepoint detection function. In addition to functions for the statistical analysis, NetworkChange provides visualization functions for summary of the analysis results (\autoref{fig:list}). The complete guide for using core functions of the package is presented at https://github.com/jongheepark/NetworkChange as its vignette with an empirical data set analysis example. @ParkSohn2020 provide methodological details of the algorithms implemented in the package.
Acknowledgements
This work was supported by the Japan Society for the Promotion of Science Early-Career Scientists Grant [JP19K13606] to Y.S.
References
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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