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[2602.21926] Bridging Through Absence: How Comeback Researchers Bridge Knowledge Gaps Through Structural Re-emergence
Summary
This article explores the role of 'comeback researchers'—those who return to academia after a hiatus—in bridging knowledge gaps and enhancing cross-disciplinary collaboration.
Why It Matters
Understanding comeback researchers is essential for fostering inclusive academic environments and leveraging their unique contributions to knowledge transfer. This study provides data-driven insights that can inform institutional policies and support systems aimed at reintegrating these researchers into the academic community.
Key Takeaways
- Comeback researchers exhibit higher citation diversity and bridging scores compared to those who drop out.
- They demonstrate unique publication trajectories characterized by higher gap entropy.
- Predictive models using bridging features significantly outperform traditional metrics in identifying impactful researchers.
Computer Science > Social and Information Networks arXiv:2602.21926 (cs) [Submitted on 25 Feb 2026] Title:Bridging Through Absence: How Comeback Researchers Bridge Knowledge Gaps Through Structural Re-emergence Authors:Somyajit Chakraborty, Angshuman Jana, Avijit Gayen View a PDF of the paper titled Bridging Through Absence: How Comeback Researchers Bridge Knowledge Gaps Through Structural Re-emergence, by Somyajit Chakraborty and 2 other authors View PDF HTML (experimental) 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 coun...
