It is increasingly common in natural and social sciences to rely on network visualizations to explore relational datasets and illustrate findings. Such practices have been around long enough to prove that scholars find it useful to project networks in a two-dimensional space and to use their visual qualities as proxies for their topological features. Yet these practices remain based on intuition, and the foundations and limits of this type of exploration are still implicit. To fill this lack of formalization, this paper offers explicit documentation for the kind of visual network analysis encouraged by force-directed layouts. Using the example of a network of Jazz performers, band and record labels extracted from Wikipedia, the paper provides guidelines on how to make networks readable and how to interpret their visual features. It discusses how the inherent ambiguity of network visualizations can be exploited for exploratory data analysis. Acknowledging that vagueness is a feature of many relational datasets in the humanities and social sciences, the paper contends that visual ambiguity, if properly interpreted, can be an asset for the analysis. Finally, we propose two attempts to distinguish the ambiguity inherited from the represented phenomenon from the distortions coming from fitting a multidimensional object in a two-dimensional space. We discuss why these attempts are only partially successful, and we propose further steps towards a metric of spatialization quality.

在自然科学和社会科学中，越来越依赖网络可视化来探索关系数据集和说明发现。这样的实践已经存在了很长时间，足以证明学者们发现在二维空间中投影网络并使用其视觉品质作为其拓扑特征的代理很有用。然而，这些实践仍然基于直觉，而这种类型的探索的基础和局限性仍然是隐含的。为了弥补这种形式化的不足，本文为受力导向布局鼓励的视觉网络分析提供了明确的文档。以爵士乐表演者网络，从维基百科中提取的乐队和唱片公司为例，本文提供了有关如何使网络更具可读性以及如何解释其视觉特征的指南。它讨论了如何将网络可视化的内在模糊性用于探索性数据分析。承认模糊性是人文科学和社会科学中许多关系数据集的特征，因此论文认为，视觉歧义如果得到正确解释，则可以成为分析的资产。最后，我们提出了两种尝试来区分从表示的现象继承的歧义和在二维空间中拟合多维对象而产生的失真。我们讨论了为什么这些尝试仅能部分成功，