2023 ASEE Annual Conference & Exposition
Race and Collaboration in Computer Science: A Network Science Approach
Presented at Equity, Culture & Social Justice in Education Division (EQUITY) Technical Session 9
This research paper discusses the creation and analysis of the collaboration networks of computer scientists in the context of race. It is well known that there is a lack of racial diversity in computer science (CS). Recent events such as the Black Lives Matter protests of 2020 led to various organizational commitments to antiracism across the tech industry as well as academia. Although there is an increased focus on the systemic inequalities resulting from technologies such as healthcare, recidivism, and facial recognition software, there is also a need to examine the inequalities present in the professional trajectories of computer scientists from historically excluded racial groups. Analyzing collaboration networks (i.e., mapped connections between scholars and their coauthors) through the lens of race can provide further insight into the inequities present in computing environments.
In this work, data from the dblp computer science bibliography was used to create the collaboration networks of a sample of 147 Black and Latinx CS doctorate recipients. Specifically, we analyzed the resulting network for each recipient, where two authors are connected by an edge if they coauthored a publication. Typical network properties such as degree (the average number of unique coauthors), density (the proportion of possible coauthorships present), and clustering (the degree to which scholars coauthor with the same people) are investigated and compared to analogous measures reported in the discipline’s overall collaboration networks that have neglected to consider race [1], [2]. Using the lens of explicit and implicit marginalization of scholars of color, we posit that this analysis uncovers “hidden” structural mechanisms that impact the success of computer scientists of color. That is, the overrepresentation of CS Ph.D.’s identifying as white or Asian leads to an “average” or “typical” measure of a computer scientist’s network that is heavily skewed in their favor.
Since research correlates collaboration networks with scholarly productivity, citation counts, and career development and success, this analysis demonstrates the far-reaching impact that a lack of diversity in CS has on Black and Latinx scholars and, further, provides opportunities to influence future intervention strategies designed to correct hidden mechanisms impeding their success.
[1] M. Franceschet, "Collaboration in Computer Science: A Network Science Approach," Journal of the American Society for Information Science and Technology, vol. 62, iss. 10, pp. 1992-2012, 2011.
[2] A.M. Jaramillo, H.T.P. Williams, N. Perra, and R. Menezes, “The Community Structure of Collaboration Networks in Computer Science and its Impact on Scientific Production and Consumption,” arXiv:2207.09800v1 [cs.SI], July 2022.
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