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Change we can believe in: Comparing longitudinal network modelson consistency, interpretability and predictive power
ETH Zurich, Switzerland.
University of Manchester, UK.
Geneva University, Switzerland.
Linköping University, Department of Management and Engineering, The Institute for Analytical Sociology, IAS. Linköping University, Faculty of Arts and Sciences. University of Groningen, The Netherlands.ORCID iD: 0000-0002-9097-0873
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2017 (English)In: Social Networks, ISSN 0378-8733, E-ISSN 1879-2111, Vol. 52, 180-191 p.Article in journal (Refereed) Published
Abstract [en]

While several models for analysing longitudinal network data have been proposed, their main differ-ences, especially regarding the treatment of time, have not been discussed extensively in the literature.However, differences in treatment of time strongly impact the conclusions that can be drawn from data.In this article we compare auto-regressive network models using the example of TERGMs – a temporalextensions of ERGMs – and process-based models using SAOMs as an example. We conclude that theTERGM has, in contrast to the ERGM, no consistent interpretation on tie-level probabilities, as well as noconsistent interpretation on processes of network change. Further, parameters in the TERGM are stronglydependent on the interval length between two time-points. Neither limitation is true for process-basednetwork models such as the SAOM. Finally, both compared models perform poorly in out-of-sampleprediction compared to trivial predictive models.

Place, publisher, year, edition, pages
Elsevier, 2017. Vol. 52, 180-191 p.
National Category
Sociology
Identifiers
URN: urn:nbn:se:liu:diva-142463DOI: 10.1016/j.socnet.2017.08.001ISI: 000416496300017OAI: oai:DiVA.org:liu-142463DiVA: diva2:1153527
Available from: 2017-10-30 Created: 2017-10-30 Last updated: 2017-12-15

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The full text will be freely available from 2019-08-23 14:10
Available from 2019-08-23 14:10

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Citation style
  • apa
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  • nn-NB
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  • Other locale
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Output format
  • html
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  • asciidoc
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