Bridging Through Absence: How Comeback Researchers Bridge Knowledge Gaps Through Structural Re-emergence

TL;DR

This paper explores how comeback researchers re-engage with science after inactivity, demonstrating their significant role in bridging knowledge gaps and outperforming traditional metrics in predicting their impactful contributions.

Contribution

It introduces a data-driven approach to identify comeback researchers and analyzes their unique structural and semantic behaviors in scientific networks.

Findings

Comeback researchers cite 126% more distinct communities.

They have 7.6% higher bridging scores.

Predictive models achieve 97% ROC-AUC using their features.

Abstract

Understanding the role of researchers who return to academia after prolonged inactivity, termed "comeback researchers", is crucial for developing inclusive models of scientific careers. This study investigates the structural and semantic behaviors of comeback researchers, focusing on their role in cross-disciplinary knowledge transfer and network reintegration. Using the AMiner citation dataset, we analyze 113,637 early-career researchers and identify 1,425 comeback cases based on a three-year-or-longer publication gap followed by renewed activity. We find that comeback researchers cite 126% more distinct communities and exhibit 7.6% higher bridging scores compared to dropouts. They also demonstrate 74% higher gap entropy, reflecting more irregular yet strategically impactful publication trajectories. Predictive models trained on these bridging- and entropy-based features achieve a 97% ROC-AUC, far outperforming the 54% ROC-AUC of baseline models using traditional metrics like publication count and h-index. Finally, we substantiate these results via a multi-lens validation. These findings highlight the unique contributions of comeback researchers and offer data-driven tools for their early identification and institutional support.