- Award ID(s):
- 2211744
- PAR ID:
- 10430768
- Date Published:
- Journal Name:
- Network Science
- ISSN:
- 2050-1242
- Page Range / eLocation ID:
- 1 to 14
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Collecting network data directly from network members can be challenging. One alternative involves inferring a network from observed groups, for example, inferring a network of scientific collaboration from researchers’ observed paper authorships. In this paper, I explore when an unobserved undirected network of interest can accurately be inferred from observed groups. The analysis uses simulations to experimentally manipulate the structure of the unobserved network to be inferred, the number of groups observed, the extent to which the observed groups correspond to cliques in the unobserved network, and the method used to draw inferences. I find that when a small number of groups are observed, an unobserved network can be accurately inferred using a simple unweighted two-mode projection, provided that each group’s membership closely corresponds to a clique in the unobserved network. In contrast, when a large number of groups are observed, an unobserved network can be accurately inferred using a statistical backbone extraction model, even if the groups’ memberships are mostly random. These findings offer guidance for researchers seeking to indirectly measure a network of interest using observations of groups.more » « less
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Using social network analysis, we sought to characterize the professional collaboration and advice networks among rural science teachers. Furthermore, we explored how the characteristics of individual teachers and distance between teachers affected the likelihood of forming connections. Science teachers in publicly funded rural schools were asked whom they collaborate with and seek advice from and the mode and frequency of their communications. Results were analyzed using UCINET to calculate statistical significance of tie formation. Ties among rural teachers were sparse, with a quarter of teachers having no connections within the bounded network. In contrast to other social network studies, characteristics of individual teachers were not a significant predictor of tie formation in our population, but geographic proximity was a strong predictor. Our findings suggest that districts can support teachers in forming supportive ties by providing time, funding, and/or technology tools and training.
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Summary Models of dynamic networks—networks that evolve over time—have manifold applications. We develop a discrete time generative model for social network evolution that inherits the richness and flexibility of the class of exponential family random-graph models. The model—a separable temporal exponential family random-graph model—facilitates separable modelling of the tie duration distributions and the structural dynamics of tie formation. We develop likelihood-based inference for the model and provide computational algorithms for maximum likelihood estimation. We illustrate the interpretability of the model in analysing a longitudinal network of friendship ties within a school.
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