艺术史社交网络中等级的知觉效应
Perceptual Effects of Hierarchy in Art Historical Social Networks
摘要：
网络表征是历史社会网络分析中的一个重要课题。由人文主义学者领导的关于它们的价值和内涵的争论，今天比以往任何时候都更有意义，因为这些表征作为历史分析的支持是多么普遍。力导向网络尤其受欢迎，因为它们可以相对快速地开发，并揭示数据中的模式和结构。底层算法虽然在揭示隐藏模式方面很强大，但并没有保留历史社交网络中有意义的结构和现有的层次结构。在这篇文章中，我们质疑这种结构的外国方面，力导向布局在历史数据集中创建。我们探讨了社交网络中层次结构的重要性，并调查了层次结构-强烈存在于我们的社会结构模型中-是否会影响我们对社会网络数据的感知。我们的用户评估结果表明，分层网络表示减少认知负荷，并导致更频繁和更深入的洞察历史社交网络。我们还发现，用户报告的层次图表示的偏好。我们分析了这些研究结果，在人文研究中的力量导向的网络表征的价值更广泛的讨论，并介绍了开放的问题，为今后的工作在这方面的研究。
英文摘要：
Network representation is a crucial topic in historical social network analysis. The debate around their value and connotations, led by humanist scholars, is today more relevant than ever, seeing how common these representations are as support for historical analysis. Force-directed networks, in particular, are popular as they can be developed relatively quickly, and reveal patterns and structures in data. The underlying algorithms, although powerful in revealing hidden patterns, do not retain meaningful structure and existing hierarchies within historical social networks. In this article, we question the foreign aspect of this structure that force-directed layout create in historical datasets. We explore the importance of hierarchy in social networks, and investigate whether hierarchies -- strongly present within our models of social structure -- affect our perception of social network data. Results from our user evaluation indicate that hierarchical network representations reduce cognitive load and leads to more frequent and deeper insights into historical social networks. We also find that users report a preference for the hierarchical graph representation. We analyse these findings in light of the broader discussion on the value of force-directed network representations within humanistic research, and introduce open questions for future work in this line of research.
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。
