解决作者同名问题以提高合著网络结构的分辨率
Resolving Author Name Homonymy to Improve Resolution of Structures in Co-author Networks
摘要：
本文研究了作者同名对大规模合著网络的影响，并提出了一种简单、有效、可扩展和可推广的算法来改善这种影响。我们评估该算法的性能，以提高介观网络结构的分辨率。为此，我们建立了一个作者姓名样本的基础事实，该样本在统计上代表了合著者网络中不同类型的节点，并通过它们在网络连通性中的作用来区分。最后，我们观察到，这种区别的节点角色的网络的介观结构的基础上，结合量化的作者姓名的共性，提出了一种新的方法来评估网络失真的同音异义和分析减少失真的网络消歧后，不需要地面真相采样。
英文摘要：
We investigate how author name homonymy distorts clustered large-scale co-author networks, and present a simple, effective, scalable and generalizable algorithm to ameliorate such distortions. We evaluate the performance of the algorithm to improve the resolution of mesoscopic network structures. To this end, we establish the ground truth for a sample of author names that is statistically representative of different types of nodes in the co-author network, distinguished by their role for the connectivity of the network. We finally observe that this distinction of node roles based on the mesoscopic structure of the network, in combination with a quantification of author name commonality, suggests a new approach to assess network distortion by homonymy and to analyze the reduction of distortion in the network after disambiguation, without requiring ground truth sampling.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
