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Network analysis has become a well-recognized methodology in physics education research (PER), with study topics including student performance and persistence, faculty change, and the structure of conceptual networks. The social network analysis side of this work has focused on quantitative analysis of whole-network cases, such as the structure of networks in single classrooms. Egocentric or personal network approaches are largely unexplored, and qualitative methods are underdeveloped. In this paper, we outline theoretical and practical differences between two major network paradigms—whole-network and egocentric—and introduce theoretical frameworks and methodological considerations for egocentric studies. We also describe qualitative and mixed-methods approaches that are currently missing from the PER literature. We identify areas where these additional network methods may be of particular interest to physics education researchers and end by discussing example cases and implications for new PER studies. Published by the American Physical Society2024 

Despite knowing physics and astronomy doctoral programs are laden with identity-based inequities, they continue to push minoritized students to the margins. This qualitative social network analysis of 100 women and/or lesbian, gay, bisexual, transgender, queer, and more ( $\mathrm{LGBT}+$) physics and astronomy Ph.D.’s explores how minoritized physics and astronomy students utilize social networks to navigate departmental exclusion. Our findings indicate that many of the participants’ identities were often unacknowledged or negatively addressed within their graduate education, with only four participants reporting a positive or favorable experience during this period of their career. Direct support from peers, faculty, and identity-based affinity groups was necessary for participants to navigate their educations. This study demonstrated that generic best practices often cannot fully support the diverse range of persons who come to physics and that identity-neutral values in physics further isolate students by insinuating that their own minoritized experiences are not valid. Published by the American Physical Society2024 

Active learning is broadly shown to improve student outcomes as compared with traditional lecture, but more work must be done to distinguish outcomes between different types of active learning. We collected self-reported student social network data at early and late-semester times in a Peer Instruction classroom. The subsequent networks are modeled using exponential random graph models (ERGMs), which are a family of statistical models used with relational data, like social networks. We discuss preliminary findings using this method for a Peer Instruction class. The best-fit ERGM predicts long "chains" of student edges, such as might arise from students talking along rows in the lecture hall. ERGMs appear to be a promising method for quantifying network topology in active learning classrooms.