Modeling Change in Networks

Jonas M. B. Haslbeck; Oisín Ryan; Han van der Maas; Lourens J. Waldorp

doi:10.4324/9781003111238-14

Modeling Change in Networks

Jonas M. B. Haslbeck, Oisín Ryan, Han van der Maas, Lourens J. Waldorp

Chair Hamaker

University of Amsterdam

Research output: Chapter in Book/Report/Conference proceeding › Chapter › Academic › peer-review

Abstract

This chapter discusses time-varying network models, which allow for network models with parameters that change across time. These models are relevant when one is interested in how individuals change across time. The chapter introduces the reader to time-varying models and discusses various methods to estimate them, which make different assumptions about the true time-varying nature of parameters. Finally, the chapter illustrates one of the methods by fitting a time-varying Gaussian graphical model (GGM) to a time series of mood related measurements.

Original language	English
Title of host publication	Network Psychometrics with R
Subtitle of host publication	A Guide for Behavioral and Social Scientists
Editors	Adela-Maria Isvoranu, Sacha Epskamp, Lourens J. Waldorp, Denny Borsboom
Place of Publication	London
Publisher	Routledge
Chapter	11
Number of pages	17
Edition	1
ISBN (Electronic)	9781003111238
ISBN (Print)	9780367628765, 9780367612948
DOIs	https://doi.org/10.4324/9781003111238-14
Publication status	Published - 31 Mar 2022

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10.4324/9781003111238-14

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title = "Modeling Change in Networks",

abstract = "This chapter discusses time-varying network models, which allow for network models with parameters that change across time. These models are relevant when one is interested in how individuals change across time. The chapter introduces the reader to time-varying models and discusses various methods to estimate them, which make different assumptions about the true time-varying nature of parameters. Finally, the chapter illustrates one of the methods by fitting a time-varying Gaussian graphical model (GGM) to a time series of mood related measurements.",

author = "Haslbeck, {Jonas M. B.} and Ois{\'i}n Ryan and {van der Maas}, Han and Waldorp, {Lourens J.}",

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editor = "Adela-Maria Isvoranu and Sacha Epskamp and Waldorp, {Lourens J.} and Denny Borsboom",

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Modeling Change in Networks. / Haslbeck, Jonas M. B.; Ryan, Oisín; van der Maas, Han et al.
Network Psychometrics with R: A Guide for Behavioral and Social Scientists. ed. / Adela-Maria Isvoranu; Sacha Epskamp; Lourens J. Waldorp; Denny Borsboom. 1. ed. London: Routledge, 2022.

Research output: Chapter in Book/Report/Conference proceeding › Chapter › Academic › peer-review

TY - CHAP

T1 - Modeling Change in Networks

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AU - Ryan, Oisín

AU - van der Maas, Han

AU - Waldorp, Lourens J.

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Y1 - 2022/3/31

N2 - This chapter discusses time-varying network models, which allow for network models with parameters that change across time. These models are relevant when one is interested in how individuals change across time. The chapter introduces the reader to time-varying models and discusses various methods to estimate them, which make different assumptions about the true time-varying nature of parameters. Finally, the chapter illustrates one of the methods by fitting a time-varying Gaussian graphical model (GGM) to a time series of mood related measurements.

AB - This chapter discusses time-varying network models, which allow for network models with parameters that change across time. These models are relevant when one is interested in how individuals change across time. The chapter introduces the reader to time-varying models and discusses various methods to estimate them, which make different assumptions about the true time-varying nature of parameters. Finally, the chapter illustrates one of the methods by fitting a time-varying Gaussian graphical model (GGM) to a time series of mood related measurements.

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DO - 10.4324/9781003111238-14

M3 - Chapter

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SN - 9780367612948

BT - Network Psychometrics with R

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A2 - Epskamp, Sacha

A2 - Waldorp, Lourens J.

A2 - Borsboom, Denny

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Modeling Change in Networks

Abstract

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