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1. When can networks be inferred from observed groups?

   https://doi.org/10.1017/nws.2024.6  

   Neal, Zachary P (June 2024, Network Science)

   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. 

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2. Uncovering the Connections Among Rural Science Teachers: A Social Network Analysis

   https://doi.org/10.1177/23328584241253821  

   Poulsen, Tracy; Leary, Heather; Daly, Alan; Sansom, Rebecca (May 2024, AERA Open)

   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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3. Inferring signed networks from preschoolers’ observed parallel and social play

   https://doi.org/10.1016/j.socnet.2022.07.002  

   Neal, Jennifer Watling; Neal, Zachary P; Durbin, C Emily (October 2022, Social Networks)

   Early childhood is an important developmental period for network formation. However, the observational methods used for measuring young children’s networks present challenges for capturing both positive and negative ties. To overcome these challenges, we explored the use of a bipartite projection backbone model for inferring both negative and positive ties from observational data of children’s play. Using observational data collected in one 3-year-old (N = 17) and one 4-year-old (N = 18) preschool classroom, we examined whether patterns of homophily, triadic closure, and balance in networks inferred using this method matched theoretical and empirical expectations from the early childhood literature. Consistent with this literature, we found that signed networks inferred using a backbone model exhibited gender homophily in positive ties and gender heterophily in negative ties. Additionally, networks inferred from social play exhibited more closed and balanced triads than networks inferred from parallel play. These findings offer evidence of the validity of bipartite projection backbone models for inferring signed networks from preschoolers’ observed play. 

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4. Teaching-focused social networks among college faculty: exploring conditions for the development of social capital

   https://doi.org/10.1007/s10734-018-0331-5  

   Benbow, Ross J.; Lee, Changhee (January 2018, Higher Education)

   Scholars have long recognized that teachers’ social interactions play an important role in their learning and professional development. Still, while a growing body of research shows that teaching-focused social ties can give precollege educators access to valuable information, knowledge, and advice—or "social capital"—that improves professional practice and student learning, empirical, mixed methods studies on the phenomenon in the higher education sector are rare, and few investigate what conditions are necessary for these social ties to develop among college instructors. Focusing on college faculty in 17 associate- and baccalaureate-level institutions in one U.S. city, this study uses survey and interview data to explore the connections between structural and positional educator characteristics and the "social networks," or compilations of social ties, in which faculty reported discussing teaching. Regression analyses of survey responses (n = 244) indicate that fewer years of teaching experience, the time faculty take preparing to teach, discipline, and institution type are correlated with social network dimensions linked to improved professional practice. An inductive analysis of interview data from a subset of faculty (n = 22) supplements survey findings with descriptions of how teaching experience, organizational support, and other factors constrain and reinforce the development of teaching-focused social ties. Results confirm and extend prior research indicating that the development of teaching-focused social networks and the accrual of ties linked to social capital demand faculty and organizational investment. Findings also suggest that leaders hoping to foster beneficial ties should tailor instructional initiatives to more closely align with faculty experience and time commitments. 

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5. Adoption of Pedagogical Innovations: Social Networks of Engineering Education Guilds

   https://doi.org/10.3390/educsci13111102  

   Riley, Darby; Mallouk, Kaitlin; Faber, Courtney; Coso Strong, Alexandra (November 2023, Education Sciences)

   This work investigates how innovations propagate through two professional networks (guilds): the Kern Entrepreneurial Engineering Network (KEEN) and the Consortium to Promote Reflection in Engineering Education (CPREE). Previous research has demonstrated that the adoption of pedagogical innovations is supported by the socialization of the innovation among potential adopters. In this work, we use social network analysis to explore the impact of professional connections on innovation adoption. Our research questions are: (1) How does overall social structure differ between guilds? (2) How do measures of social network structures relate to innovation adoption? A survey was distributed to members of KEEN and CPREE to capture the interactions respondents had while adopting the guild’s innovation. Social networks were generated for each guild and each respondent. These networks were analyzed to identify relationships between social network measures and the frequency of use of the innovation. Responses to open-ended questions were analyzed using thematic coding. The guilds’ overall structures impacted the formation and structure of distinct clusters/cliques, but these differing structures did not appear to affect sustained adoption. Individuals’ ego networks demonstrated a weak negative correlation between the frequency of adoption and the individual’s ego network density. Our results imply that having a diverse network exposes instructors to more ideas or allows them to see one idea from many perspectives. 

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