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

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 75 Indian villages collected with a name-generator design, and a Nicaraguan community collected with a roster-based 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.

References in corpus (11)

• Variational Inference: A Review for Statisticians
• Stochastic blockmodels and community structure in networks
• Network structure from rich but noisy data
• Community detection, link prediction, and layer interdependence in multilayer networks
• Friendship networks and social status
• Estimating network structure from unreliable measurements
• Reconstructing networks with unknown and heterogeneous errors
• Bayesian inference of network structure from unreliable data
• Mutual Assent or Unilateral Nomination? A Performance Comparison of Intersection and Union Rules for Integrating Self-reports of Social Relationships
• Generative model for reciprocity and community detection in networks
• Community detection and reciprocity in networks by jointly modeling pairs of edges

Cited by in corpus (2)

• The latent cognitive structures of social networks
• Anomaly, reciprocity, and community detection in networks