基于首字母的姓名消歧对大规模合著网络测量的失真效应
Distortive Effects of Initial-Based Name Disambiguation on Measurements of Large-Scale Coauthorship Networks
摘要：
在大规模的合著网络研究中，学者们经常依赖姓名首字母来解决姓名歧义。这种方法有错误地合并或分割作者身份的风险。基于首字母的消歧的使用已经被这样的假设所证明，即这种错误不会太多地影响研究结果。本文通过分析生物学、计算机科学、纳米科学、神经科学和物理学这五个学术领域的合著网络以及跨学科期刊PNAS来验证这一假设。本研究的数据集中的名称实例基于从先前的算法消歧解决方案获得的语义学消歧。我们使用消歧数据作为地面实况的代理来测试三种类型的初始消歧的性能。我们的研究结果表明，基于初始值的消歧可以歪曲合作网络的统计特性：它缩小了唯一作者的数量，组件的数量，平均最短路径，聚类系数和重复性，而它膨胀了平均生产力，密度，平均每名作者的合著者数量和最大组件的大小。此外，平均而言，前10名生产或合作作者中有一半以上退出了名单。亚洲人的名字被发现占大多数的错误识别的初始为基础的消歧，由于他们共同的姓氏和名字的首字母。
英文摘要：
Scholars have often relied on name initials to resolve name ambiguities in large-scale coauthorship network research. This approach bears the risk of incorrectly merging or splitting author identities. The use of initial-based disambiguation has been justified by the assumption that such errors would not affect research findings too much. This paper tests this assumption by analyzing coauthorship networks from five academic fields - biology, computer science, nanoscience, neuroscience, and physics - and an interdisciplinary journal, PNAS. Name instances in datasets of this study were disambiguated based on heuristics gained from previous algorithmic disambiguation solutions. We use disambiguated data as a proxy of ground-truth to test the performance of three types of initial-based disambiguation. Our results show that initial-based disambiguation can misrepresent statistical properties of coauthorship networks: it deflates the number of unique authors, number of component, average shortest paths, clustering coefficient, and assortativity, while it inflates average productivity, density, average coauthor number per author, and largest component size. Also, on average, more than half of top 10 productive or collaborative authors drop off the lists. Asian names were found to account for the majority of misidentification by initial-based disambiguation due to their common surname and given name initials.
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