TY - JOUR
T1 - Latent network models to account for noisy, multiply reported social network data
AU - De Bacco, Caterina
AU - Contisciani, Martina
AU - Cardoso-Silva, Jonathan
AU - Safdari, Hadiseh
AU - Borges, Gabriela Lima
AU - Baptista, Diego
AU - Sweet, Tracy
AU - Young, Jean Gabriel
AU - Koster, Jeremy
AU - Ross, Cody T.
AU - McElreath, Richard
AU - Redhead, Daniel
AU - Power, Eleanor A.
N1 - Publisher Copyright:
© (RSS) Royal Statistical Society 2023.
PY - 2023/2/8
Y1 - 2023/2/8
N2 - 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.
AB - 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.
KW - Social network data
KW - latent network
KW - mutuality
KW - network measurement
KW - reliability
KW - variational inference
UR - http://www.scopus.com/inward/record.url?scp=85164252158&partnerID=8YFLogxK
U2 - 10.1093/jrsssa/qnac004
DO - 10.1093/jrsssa/qnac004
M3 - Article
SN - 0964-1998
VL - 186
SP - 355
EP - 375
JO - Journal of the Royal Statistical Society. Series A: Statistics in Society
JF - Journal of the Royal Statistical Society. Series A: Statistics in Society
IS - 3
ER -