Latent Space Approaches to Social Network Analysis
Abstract
Network models are widely used to represent relational information among interacting units. In studies of social networks, recent emphasis has been placed on random graph models where the nodes usually represent individual social actors and the edges represent the presence of a specified relation between actors. We develop a class of models where the probability of a relation between actors depends on the positions of individuals in an unobserved “social space.” We make inference for the social space within maximum likelihood and Bayesian frameworks, and propose Markov chain Monte Carlo procedures for making inference on latent positions and the effects of observed covariates. We present analyses of three standard datasets from the social networks literature, and compare the method to an alternative stochastic blockmodeling approach. In addition to improving on model fit for these datasets, our method provides a visual and interpretable model-based spatial representation of social relationships and improves on existing methods by allowing the statistical uncertainty in the social space to be quantified and graphically represented.
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Although the Bayesian modeling approach is not new to network analysis in general, it remains relatively rare.
“Although the Bayesian modeling approach is not new to network analysis in general [25], [26], it remains relatively rare.”
From Spatial Guilds in the Serengeti Food Web Revealed by a Bayesian Group Model (2011) · cites this paper as [25]
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“…for various tasks and applications, such as link prediction in knowledge bases (Nickel et al., 2011; Bor-des et al., 2013) or in social networks (Hoff et al., 2002), text disambiguation (Ganea & Hofmann, 2017), word hyper-nymy (Shwartz et al., 2016), textual entailment (Rockt¨aschel et al.,…”
BackgroundSpatial Guilds in the Serengeti Food Web Revealed by a Bayesian Group Model
Baskerville, E. B., Dobson, A. P., Bedford, T., Allesina, S., Anderson, T. M., & Pascual, M. (2011) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1002321 · Cited by 107 · see citing works
“Although the Bayesian modeling approach is not new to network analysis in general [25,26], it remains relatively rare.”
BackgroundMethod
- Authors
- Hoff, P. D., Raftery, A. E., & Handcock, M. S.
- Affiliations
- University of Washington (US)
- Venue
- Journal of the American Statistical Association
- Publisher
- Informa UK Limited
- Published
- 2002-12
- Volume
- 97
- Issue
- 460
- Pages
- 1090-1098
- ISSN
- 0162-1459 (ISSN-L)
- 1537-274X
- OpenAlex
- W2066459332
- Cited by
- 1,917
- References
- 0
- Citations by year
- 2026: 69 · 2025: 110 · 2024: 97 · 2023: 115 · 2022: 100 · 2021: 134 · 2020: 146 · 2019: 161 · 2018: 142 · 2017: 135 · 2016: 97 · 2015: 98 · 2014: 98 · 2013: 82 · 2012: 70
- FWCI
- 5.19
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of the American Statistical Association (publishedVersion)
- Defense Technical Information Center (DTIC) (submittedVersion)
- Topics
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Complex Network Analysis Techniques
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Spatial and Panel Data Analysis
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Opinion Dynamics and Social Influence
- Keywords
- latent space models, social networks, random graph models, latent positions, Markov chain Monte Carlo, covariate effects, uncertainty quantification, network inference
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