Measuring reciprocity: Double sampling, concordance, and network construction

@inproceedings{Ready2021MeasuringRD,
  title={Measuring reciprocity: Double sampling, concordance, and network construction},
  author={Elspeth Ready and Eleanor A. Power},
  year={2021}
}
Reciprocity—the mutual provisioning of support/goods—is a pervasive feature of social life. Di- rected networks provide a way to examine the structure of reciprocity in a community. However, measuring social networks involves assumptions about what relationships matter and how to elicit them, which may impact observed reciprocity. In particular, the practice of aggregating multiple sources of data on the same relationship (e.g., “double-sampled” data, where both the “giver” and “re- ceiver” are… 
Measuring reciprocity: Double sampling, concordance, and network construction
Abstract Reciprocity—the mutual provisioning of support/goods—is a pervasive feature of social life. Directed networks provide a way to examine the structure of reciprocity in a community. However,
Latent Network Models to Account for Noisy, Multiply-Reported Social Network Data
TLDR
A probabilistic model that incorporates ties reported by multiple individuals to estimate the unobserved network structure and explicitly incorporates a term for “mutuality,” the tendency to report ties in both directions involving the same alter, is proposed.
Community detection and reciprocity in networks by jointly modeling pairs of edges
TLDR
A probabilistic generative model and an efficient algorithm to both perform community detection and capture reciprocity in networks that overcomes the limitations of both standard algorithms and recent models that incorporate reciprocity through a pseudo-likelihood approximation are presented.