Edge interventions can mitigate demographic and prestige disparities in the computer science coauthorship network

Document Type

Conference Proceeding

Role

Author

Published In

FAccT '26: The 2026 ACM Conference on Fairness, Accountability, and Transparency

First Page

6403

Last Page

6437

Publication Date

6-25-2026

Abstract

Social factors such as demographic traits and institutional prestige structure the creation and dissemination of ideas in academic publishing. Such effects can be observed in how central or peripheral a researcher is within their field's coauthorship network. Here we investigate inequities in network centrality in a hand-collected data set of 5,670 U.S.-based faculty employed in Ph.D.-granting computer science departments and their DBLP coauthorship connections. We introduce novel demographic labeling algorithms that combine name- and perception-based labels, and show that these algorithms have high accuracy relative to self-reported demographic labels. We find that women, minoritized races, and faculty without parents employed in tenure-track academic positions tend to be less central in the computer science coauthorship network, implying worse access to and ability to spread information. Coauthorship centrality is also highly correlated with prestige, such that faculty at top-ranked departments tend to occupy positions in the network's core, while those at low-ranked departments tend to occupy positions in the periphery. We show that these disparities can be mitigated using simulated edge interventions, interpreted as facilitated scientific collaborations. The intervention increases the centrality of target individuals, chosen independently of the network structure, by linking them with researchers located at top-ranked institutions. When applied to scholars during their Ph.D., the intervention also improves the predicted institutional rank of their faculty job. This work uncovers social inequities in order to address them. By selecting scholars for intervention based on their demographics and institutional prestige, we can improve their centrality in the coauthorship network, potentially improving job placement and long-term academic success.

Keywords

network fairness, edge interventions, demographic inference, science of science, coauthorship networks

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