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

Caterina De Bacco, Martina Contisciani, Jon Cardoso Silva, Hadiseh Safdari, Gabriela Lima Borges, Diego Baptista, Tracy Sweet, Jean-Gabriel Young, Koster Jeremy, Cody T Ross, Richard McElreath, Daniel Redhead and Eleanor Power

LSE Research Online Documents on Economics from London School of Economics and Political Science, LSE Library

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.

Keywords: social network data; mutuality; reliability; variational inference; latent network; network measurement (search for similar items in EconPapers)
JEL-codes: C1 (search for similar items in EconPapers)
Pages: 21 pages
Date: 2023-07-31
New Economics Papers: this item is included in nep-ecm, nep-net and nep-ure
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Published in Journal of the Royal Statistical Society. Series A: Statistics in Society, 31, July, 2023, 186(3), pp. 355 - 375. ISSN: 0964-1998

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