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A Separable Model for Dynamic Networks
Open accessAbstract
Summary: Models of dynamic networks—networks that evolve over time—have manifold applications. We develop a discrete time generative model for social network evolution that inherits the richness and flexibility of the class of exponential family random-graph models. The model—a separable temporal exponential family random-graph model—facilitates separable modelling of the tie duration distributions and the structural dynamics of tie formation. We develop likelihood-based inference for the model and provide computational algorithms for maximum likelihood estimation. We illustrate the interpretability of the model in analysing a longitudinal network of friendship ties within a school.
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Citations
Cited by
Modern temporal network theory: a colloquium
Holme, P. (2015) · The European Physical Journal B · doi:10.1140/epjb/e2015-60657-4 · Cited by 734
Exponential-family random graph models for valued networks
Krivitsky, P. N. (2012) · Electronic Journal of Statistics · doi:10.1214/12-ejs696 · Cited by 295
Self-citationJenness, S. M., Weiss, K. M., Goodreau, S. M., Gift, T., Chesson, H., Hoover, K. W., Smith, D. K., Liu, A. Y., Sullivan, P. S., & Rosenberg, E. S. (2017) · Clinical Infectious Diseases · doi:10.1093/cid/cix439 · Cited by 224
Metadata
- Authors
- Krivitsky, P. N., & Handcock, M. S.
- Venue
- Journal of the Royal Statistical Society Series B: Statistical Methodology
- Publisher
- Oxford University Press (OUP)
- Published
- 2013-03-20
- Volume
- 76
- Issue
- 1
- Pages
- 29-46
- ISSN
- 1369-7412 (ISSN-L)
- 1467-9868
- OpenAlex
- W1970034774
- Cited by
- 484
- References
- 0
- FWCI
- 16.26
- Type
- journal article
- Language
- en
- Open access
- green
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- CoChat. (2026). Citation check: A Separable Model for Dynamic Networks. https://app.cochat.ai/papers/10.1111/rssb.12014
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- Krivitsky, P. N., & Handcock, M. S. (2014). A Separable Model for Dynamic Networks. Journal of the Royal Statistical Society Series B: Statistical Methodology. https://doi.org/10.1111/rssb.12014
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
