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The Graduate Research Identity Development program (GRID) is an initiative in the College of Engineering at North Carolina A&T State University, sponsored by the National Science Foundation since 2019. The program offers seminar-type lectures supplemented with activities designed to help graduate students develop critical skills for research-based careers. The program is focused on graduate engineering students but is open to graduate students from all programs. Students also choose mentors from within and outside the university with the goal of increasing their sense of belonging to the field and their identities as research engineers. As part of this program, a pilot study is in progress, aimed at performing a full-scale network analysis of student interactions. A web-based survey was administered to collect information about students in and outside the College of Engineering who participate in the GRID program sessions. The survey was designed to collect information on the relationship networks (or lack thereof) that students are involved in as they matriculate through their graduate program. It assesses things such as how and where the students interact with one another, members of faculty and staff, and with contacts from intramural and extramural organizations. Several items are also used to assess students’ perceptions of themselves as research engineers. In this paper, we focus on the interactions of students in the classroom. More specifically, we form networks based on the student answers about the classes they have taken in different departments. We then analyze the resultant networks and contrast certain graph theoretic properties to students’ scores on the research engineer identity items. Do students that are in the periphery, or students that have more connections attain higher research engineer identity scores? Do students that form complete subnetworks (cliques) or core-periphery structures (induced stars) have higher scores than others? This paper presents the findings from this pilot study from the network analysis on this cohort of students. In summary, we find that students with high eigenvector centrality scores and those who form larger cliques possess significantly higher research engineer identity scores.