The term Behavioral Networks describes networks that contain relational information on human behavior. This ranges from social networks that contain friendships or cooperations between individuals, to navigational networks that contain geographical or web navigation, and many more. Understanding the forces driving behavior within these networks can be beneficial to improving the underlying network, for example, by generating new hyperlinks on websites, or by proposing new connections and friends on social networks. Previous approaches considered different hypotheses on a single network and evaluated which hypothesis fits best. These hypotheses can represent human intuition and expert opinions or be based on previous insights. In this work, we extend these approaches to enable the comparison of a single hypothesis between multiple networks. We unveil several issues of naive approaches that potentially impact comparisons and lead to undesired results. Based on these findings, we propose a framework with five flexible components that allow addressing specific analysis goals tailored to the application scenario. We show the benefits and limits of our approach by applying it to synthetic data and several real-world datasets, including web navigation, bibliometric navigation, and geographic navigation. Our work supports practitioners and researchers with the aim of understanding similarities and differences in human behavior between environments.

行为网络一词描述了包含人类行为相关信息的网络。其范围从包含个人之间的友谊或合作的社交网络到包含地理或网络导航的导航网络等等。了解这些网络内驱动行为的力量有助于改善底层网络，例如，通过在网站上生成新的超链接，或在社交网络上提出新的联系和朋友。以前的方法考虑了单个网络上的不同假设，并评估哪种假设最适合。这些假设可以代表人类直觉和专家意见，也可以基于先前的见解。在这项工作中，我们扩展了这些方法，以便能够比较多个网络之间的单个假设。我们揭示了一些幼稚方法的问题，这些问题可能会影响比较并导致不良结果。基于这些发现，我们提出了一个包含五个灵活组件的框架，可以实现针对应用场景定制的特定分析目标。我们通过将我们的方法应用于合成数据和几个现实世界的数据集（包括网络导航、文献计量导航和地理导航）来展示该方法的优点和局限性。我们的工作支持从业者和研究人员了解环境之间人类行为的相似性和差异。