arXiv subjects

Yanmeng Xing

Publications and source records attributed to Yanmeng Xing.

3 recordsLinked to original sources

arXiv2024

The independence paradox in scientific careers

Establishing an independent academic identity is a central yet insufficiently understood challenge for early-career researchers. However, limited resources and mentor-driven research agendas often constrain early efforts toward autonomy. To provide large-scale quantitative evidence on how junior researchers develop independence, we introduce a framework that traces how mentees diverge from their mentors in both research topics and collaboration networks, and how these divergences relate to long-term scientific impact. Analyzing over 500,000 mentee-mentor pairs in Chemistry, Neuroscience, and Physics across six decades, we find that high-impact scientists often initiate work in secondary areas of their mentors' expertise while adaptively establishing distinct research trajectories. This pattern is most pronounced among mentees who eventually surpass their mentors' impact. We identify an inverted U-shaped relationship between topic divergence and mentees' enduring impact, with moderate divergence yielding the highest scientific impact, revealing an independence paradox in scientific careers. This pattern holds whether topic divergence is measured by citation network or semantic thematic distance. We further reveal that excessive direct mentor-mentee collaborations correlate with lower mentee impact, whereas expanding professional networks to include mentors' collaborators is beneficial. These findings not only offer actionable guidance for early-career researchers navigating independence but also inform institutional policies that promote mentorship structures supporting intellectual innovation and recognizing original contributions in promotion evaluations.

cs.DL↗

arXiv2022

Predicting the cascading dynamics in complex networks via the bimodal failure size distribution

Cascading failure as a systematic risk occurs in a wide range of real-world networks. Cascade size distribution is a basic and crucial characteristic of systemic cascade behaviors. Recent research works have revealed that the distribution of cascade sizes is a bimodal form indicating the existence of either very small cascades or large ones. In this paper, we aim to understand the properties and formation of such bimodal distribution of cascade sizes in complex networks, and further predict the final cascade size. We first find that the bimodal distribution of cascade sizes is ubiquitous in both synthetic and real networks. Moreover, the large cascade sizes distributed in the right peak of bimodal distribution are resulted from either the failure of nodes with high load at the first step of the cascade or multiple rounds of cascades triggered by the initial failure. Accordingly, we propose a hybrid load metric (HLM), which combines the load of the initial broken node and the load of failed nodes triggered by the initial failure, to predict the final size of cascading failures. Finally, we validate the effectiveness of HLM by computing the accuracy of identifying the cascades belonging to the right and left peaks of the bimodal distribution. The results show that HLM is a better predictor than commonly used network centrality metrics in both synthetic and real-world networks.

physics.soc-ph↗

arXiv2022

Academic mentees succeed in big groups, but thrive in small groups

Mentoring is a key component of scientific achievements, contributing to overall measures of career success for mentees and mentors. A common success metric in the scientific enterprise is acquiring a large research group, which is believed to indicate excellent mentorship and high-quality research. However, large, competitive groups might also amplify dropout rates, which are high especially among early career researchers. Here, we collect longitudinal genealogical data on mentor-mentee relations and their publication, and study the effects of a mentor's group on future academic survival and performance of their mentees. We find that mentees trained in large groups generally have better academic performance than mentees from small groups, if they continue working in academia after graduation. However, we also find two surprising results: Academic survival rate is significantly lower for (1) mentees from larger groups, and for (2) mentees with more productive mentors. These findings reveal that success of mentors has a negative effect on the academic survival rate of mentees, raising important questions about the definition of successful mentorship and providing actionable suggestions concerning career development.

physics.data-an↗