Edge interventions can mitigate demographic and prestige disparities in the Computer Science coauthorship network

2025-06-04

AI Summary

This study investigates centrality inequality in computer science co-authorship networks arising from gender, race, and institutional prestige disparities. Using a DBLP co-authorship network of 5,670 U.S. CS faculty, it identifies systematic effects of these demographic and institutional factors on network centrality. Methodologically, the work introduces “prestige-aware edge intervention”—a targeted strategy to foster cross-prestige institutional collaborations—and is the first to link such interventions to academic career outcomes, specifically postdoctoral placement prediction. It further proposes a democratic calibration algorithm integrating name-based and perception-informed demographic labels, substantially improving inference accuracy. Empirically, the intervention significantly enhances centrality for marginalized scholars; when applied during the doctoral phase, it also improves predictive accuracy for future faculty institution rankings—demonstrating the structural role of co-authorship networks in academic mobility.

Technology Category

• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community
• Application Domains: Humanities & Computational Social Science
• Reasoning under Uncertainty: Causality

Application Category

• Social Networks and Social Media: Computational social science
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

Social factors such as demographic traits and institutional prestige structure the creation and dissemination of ideas in academic publishing. One place these effects can be observed is in how central or peripheral a researcher is in the 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 algorithms for combining name- and perception-based demographic labels by maximizing alignment with self-reported demographics from a survey of faculty from our census. We find that women and individuals with minoritized race identities are less central in the computer science coauthorship network, implying worse access to and ability to spread information. Centrality is also highly correlated with prestige, such that faculty in top-ranked departments are at the core and those in low-ranked departments are in the peripheries of the computer science coauthorship network. We show that these disparities can be mitigated using simulated edge interventions, interpreted as facilitated collaborations. Our intervention increases the centrality of target individuals, chosen independently of the network structure, by linking them with researchers from highly ranked institutions. When applied to scholars during their Ph.D., the intervention also improves the predicted rank of their placement institution in the academic job market. This work was guided by an ameliorative approach: uncovering social inequities in order to address them. By targeting scholars for intervention based on institutional prestige, we are able to improve their centrality in the coauthorship network that plays a key role in job placement and longer-term academic success.

Problem

• Addressing demographic disparities in CS coauthorship network centrality
• Reducing prestige-based inequities in academic collaboration networks
• Improving job placement outcomes through simulated edge interventions

Innovation

• Edge interventions mitigate coauthorship network disparities
• Name- and perception-based demographic labeling algorithms
• Simulated facilitated collaborations improve scholar centrality