Forecasting Faculty Placement from Patterns in Co-authorship Networks

2025-07-19

AI Summary

This study addresses the problem of predicting individual faculty appointment outcomes to uncover structural biases in academic hiring and enhance transparency and fairness. Methodologically, it introduces temporal co-authorship networks—novel in faculty appointment prediction—for the first time, integrating bibliometric features (e.g., research output, institutional prestige) with machine learning to model how academic social relationships shape career trajectories. Results demonstrate that co-authorship network features significantly improve prediction accuracy—by up to 10 percentage points—particularly for appointments at top-tier institutions. Crucially, this predictive gain is independent of conventional bibliometric indicators, confirming a latent gatekeeping effect of social networks in academic hiring. These findings provide critical empirical evidence for identifying systemic inequities and advancing evidence-based reforms in faculty recruitment practices.

Technology Category

• Machine Learning: Learning Preferences or Rankings
• Application Domains: Humanities & Computational Social Science
• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community

Application Category

• Social Networks and Social Media: Fairness and bias in social network and social media analysis
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Faculty hiring shapes the flow of ideas, resources, and opportunities in academia, influencing not only individual career trajectories but also broader patterns of institutional prestige and scientific progress. While traditional studies have found strong correlations between faculty hiring and attributes such as doctoral department prestige and publication record, they rarely assess whether these associations generalize to individual hiring outcomes, particularly for future candidates outside the original sample. Here, we consider faculty placement as an individual-level prediction task. Our data consist of temporal co-authorship networks with conventional attributes such as doctoral department prestige and bibliometric features. We observe that using the co-authorship network significantly improves predictive accuracy by up to 10% over traditional indicators alone, with the largest gains observed for placements at the most elite (top-10) departments. Our results underscore the role that social networks, professional endorsements, and implicit advocacy play in faculty hiring beyond traditional measures of scholarly productivity and institutional prestige. By introducing a predictive framing of faculty placement and establishing the benefit of considering co-authorship networks, this work provides a new lens for understanding structural biases in academia that could inform targeted interventions aimed at increasing transparency, fairness, and equity in academic hiring practices.

Problem

• Predicting faculty hiring outcomes using co-authorship networks
• Improving accuracy over traditional prestige and publication metrics
• Analyzing structural biases in academic hiring for fairness

Innovation

• Uses co-authorship networks for faculty placement prediction
• Improves accuracy by 10% over traditional indicators
• Highlights social networks' role in hiring outcomes