• Corpus ID: 245353843

Latent Network Models to Account for Noisy, Multiply-Reported Social Network Data

@article{Bacco2021LatentNM,
  title={Latent Network Models to Account for Noisy, Multiply-Reported Social Network Data},
  author={Caterina De Bacco and Martina Contisciani and Jonathan Cardoso-Silva and Hadi Safdari and Diego Baptista and Tracy Morrison Sweet and Jean-Gabriel Young and Jeremy M. Koster and Cody T. Ross and Richard Mcelreath and Daniel Redhead and Eleanor A. Power},
  journal={ArXiv},
  year={2021},
  volume={abs/2112.11396}
}
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… 

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References

SHOWING 1-10 OF 75 REFERENCES
Network structure from rich but noisy data
TLDR
A technique is described that allows optimal inference of the structure of a network when the available observed data are rich but noisy, incomplete or otherwise unreliable.
Likelihoods for fixed rank nomination networks
TLDR
It is shown analytically and via simulation that the binary likelihood can provide misleading inference, particularly for certain model parameters that relate network ties to characteristics of individuals and pairs of individuals.
A guide to choosing and implementing reference models for social network analysis
TLDR
A variety of randomization procedures that generate reference models for social network analysis are reviewed to provide social network researchers with a deeper understanding of analytical approaches to enhance their confidence when tailoring reference models to specific research questions.
Reciprocity, community detection, and link prediction in dynamic networks
TLDR
A probabilistic generative model with hidden variables that integrates reciprocity and communities as structural information of networks that evolve in time that captures the reciprocity of real networks better than standard models with only community structure, while performing well at link prediction tasks.
Estimating network structure from unreliable measurements
  • M. Newman
  • Computer Science
    Physical Review E
  • 2018
TLDR
Here a general method for making estimates of network structure and properties using any form of network data, simple or complex, when the data are unreliable is developed, and example applications to a selection of social and biological networks are given.
Clustering of heterogeneous populations of networks
TLDR
A finite mixture model of the measurement process is defined and a Gibbs sampling procedure is derived that samples exactly from the full posterior distribution of model parameters, resulting in a clustering of the measured networks into groups with similar structure.
Measuring reciprocity: Double sampling, concordance, and network construction
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,
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.
An introduction to exponential random graph (p*) models for social networks
Friendship networks and social status
TLDR
It is shown that reciprocated and unreciprocated friendships obey different statistics, suggesting different formation processes, and that rankings are correlated with other characteristics of the participants that are traditionally associated with status, such as age and overall popularity as measured by total number of friends.
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