Wandering between close and apart: are scholarly domains crossable?

Haiwen Wang, Lingkun Kong, Luoyi Fu, Xinbing Wang
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Abstract

Interdisciplinary collaborations, i.e., scholarly crossdomain collaborations have generated huge impact to society, and has been previously proved to exhibit domain skewness[20]. To illustrate, scholarly cross-domain collaborations seldom emerge between irrelevant scholarly domains. In this work we address a question to determine the possible existence of scholarly cross-domain collaborations, namely: "Are scholarly domains really crossable?" Using a real-world scholarly dataset, i.e., Microsoft Academic Graph (MAG)[1] with 126 million papers collected from 53,834 domains, we take the initiative to formalize a "crossability" quantification problem, where the "crossability" serves as an index that aims to evaluate the ability of two scientific domains to establish collaborations. In doing so, we propose two metrics, i.e., co-paper ratio and hierarchical distance, where the former one is the ratio of common papers in two domains to which in a single domain, and the later one is the difference of domains' levels according to their positions in the hierarchical structure. Interestingly, we observe a peak pattern, meaning that the influence of research work, i.e., number of citations, climbs to a peak when its domains' count goes to a certain number, after which the citation count decrease sharply. Our discovery indicates that a moderate amount of domain "crossability" helps to improve the impact of research work, which, however, could be weakened under excessive "crossability". With elaborately modeling, we reproduce this peak pattern and briefly discuss the reason of the existence of peak.
徘徊在接近和分离之间:学术领域可以交叉吗?
跨学科合作,即学术跨领域合作已经对社会产生了巨大的影响,并且先前已被证明具有领域偏度bb0。举例说明,学术跨领域合作很少出现在不相关的学术领域之间。在这项工作中,我们解决了一个问题,以确定学术跨领域合作的可能存在,即:“学术领域真的可以交叉吗?”利用一个真实世界的学术数据集,即微软学术图(MAG)[1],从53,834个领域收集了1.26亿篇论文,我们主动形式化了一个“交叉性”量化问题,其中“交叉性”作为一个指标,旨在评估两个科学领域建立合作的能力。为此,我们提出了两个度量指标,即合作论文比和层次距离,其中合作论文比是两个领域的共同论文与单个领域的共同论文的比率,而层次距离是领域在层次结构中位置的水平差。有趣的是,我们观察到一个峰值模式,即当研究工作的领域数量达到一定数量时,研究工作的影响力(即被引次数)达到一个峰值,之后被引次数急剧下降。我们的发现表明,适度的领域“交叉性”有助于提高研究工作的影响,但过度的“交叉性”可能会削弱研究工作的影响。通过精细的建模,我们再现了这一峰型,并简要讨论了峰存在的原因。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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