Hyperauthorship, a phenomenon whereby there are a disproportionately large number of authors on a single paper, is increasingly common in several scientific disciplines, but with unknown consequences for network metrics used to study scientific collaboration. The validity of co-authorship as a proxy for scientific collaboration is affected by this. Using bibliometric data from publications in the field of genomics, we examine the impact of hyperauthorship on metrics of scientific collaboration, and propose a method to determine a suitable cutoff threshold for hyperauthored papers and compare co-authorship networks with and without hyperauthored works. Our analysis reveals that including hyperauthored papers dramatically impacts the structural positioning of central authors and the topological characteristics of the network, while producing small influences on whole-network cohesion measures. We present two solutions to minimize the impact of hyperauthorship: using a mathematically grounded and reproducible calculation of threshold cutoff to exclude hyperauthored papers or fractional counting to weight network results. Our findings affirm the structural influences of hyperauthored papers and suggest that scholars should be mindful when using co-authorship networks to study scientific collaboration.

翻译：超长作者名单（hyperauthorship）现象指单篇论文作者数量异常庞大，在多个科学学科中日益普遍，但对用于研究科学合作的网络指标产生了未知影响。合著关系作为科学合作替代指标的有效性因此受到干扰。利用基因组学领域出版物的文献计量数据，我们检验了超长作者名单对科学合作指标的影响，提出了一种确定超长篇论文合理截断阈值的方法，并比较了包含与排除超长篇论文的合著网络。分析表明，包含超长篇论文会显著改变核心作者的结构定位及网络的拓扑特征，但对整体网络凝聚力指标影响较小。我们提出两种解决方案以最小化超长作者名单的影响：采用基于数学推理且可复现的阈值截断法排除超长篇论文，或采用分数计数法对网络结果进行加权。研究结果证实了超长篇论文的结构性影响，并建议学者在使用合著网络研究科学合作时需保持审慎。