comeback 研究者通过跨领域引用重建学术网络，填补知识断层。
Bridging Through Absence: How Comeback Researchers Bridge Knowledge Gaps Through Structural Re-emergence
- 基于发表中断后重活跃的特征识别回流学者，分析其跨领域引用行为。
- 回流者引用的社区数多126%，桥梁得分高7.6%，知识转移更显著。
- 用桥接度与断层熵预测回流成功率达97%，远超传统指标。
理解长期中断后重返学术界的“回流研究者”的角色，对构建包容性科研生涯模型至关重要。本研究基于AMiner引文数据集，分析113,637名早期研究者，识别出1,425例回流案例（以三年及以上无发表为标准）。结果表明，回流研究者引用的学术社区数量多126%，桥梁得分高出7.6%，且断层熵达74%更高，体现其不规则但战略性强的发表轨迹。基于桥接度与熵值的预测模型实现97%的ROC-AUC，远超传统指标（如发文量、h指数）的54%。多视角验证进一步支持结论。研究揭示了回流者的独特贡献，并提供了早期识别与制度支持的数据工具。
原文摘要 · Abstract (English)
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.
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