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Latent network models to account for noisy, multiply reported social network data

De Bacco, Caterina, Contisciani, Martina, Cardoso Silva, Jon, Safdari, Hadiseh, Borges, Gabriela Lima, Baptista, Diego, Sweet, Tracy, Young, Jean-Gabriel, Jeremy, Koster, Ross, Cody T, McElreath, Richard, Redhead, Daniel and Power, Eleanor ORCID: 0000-0002-3064-2050 (2023) Latent network models to account for noisy, multiply reported social network data. Journal of the Royal Statistical Society. Series A: Statistics in Society, 186 (3). pp. 355-375. ISSN 0964-1998

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Identification Number: 10.1093/jrsssa/qnac004

Abstract

Social network data are often constructed by incorporating reports from multiple individuals. However, it is not obvious how to reconcile discordant responses from individuals. There may be particular risks with multiply reported data if people’s responses reflect normative expectations—such as an expectation of balanced, reciprocal relationships. Here, we propose a probabilistic model that incorporates ties reported by multiple individuals to estimate the unobserved network structure. In addition to estimating a parameter for each reporter that is related to their tendency of over- or under-reporting relationships, the model explicitly incorporates a term for ‘mutuality’, the tendency to report ties in both directions involving the same alter. Our model’s algorithmic implementation is based on variational inference, which makes it efficient and scalable to large systems. We apply our model to data from a Nicaraguan community collected with a roster-based design and 75 Indian villages collected with a name-generator design. We observe strong evidence of ‘mutuality’ in both datasets, and find that this value varies by relationship type. Consequently, our model estimates networks with reciprocity values that are substantially different than those resulting from standard deterministic aggregation approaches, demonstrating the need to consider such issues when gathering, constructing, and analysing survey-based network data.

Item Type: Article
Official URL: https://academic.oup.com/jrsssa?login=false
Additional Information: © 2023 The Authors
Divisions: Methodology
Subjects: H Social Sciences > HA Statistics
Date Deposited: 10 Nov 2022 10:51
Last Modified: 15 Sep 2023 17:23
URI: http://eprints.lse.ac.uk/id/eprint/117271

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