HoNVis：可视化和探索高阶网络
HoNVis: Visualizing and Exploring Higher-Order Networks
摘要：
与传统的一阶网络（FoN）不同，高阶网络（HoN）通过创建额外的节点来编码高阶依赖关系，从而提供更准确的转换描述。然而，存在没有可视化和探索工具的HoN。对于应用程序，如战略的发展，以控制物种入侵，通过全球航运，这是已知的表现出高阶的依赖关系，现有的FoN可视化工具是有限的。在本文中，我们提出了HoNVis，一种新的可视化分析框架，用于探索全球海运网络的高阶依赖关系。我们的框架利用协调的多个视图来揭示三个细节级别的网络结构（即，全局、本地和单独端口级别）。用户可以在全局级别快速识别感兴趣的端口，并指定端口以在单个端口级别调查其高阶节点。通过在地方一级探索HoN，可以调查更大规模的影响。使用全球海运网络数据，我们证明了我们的方法的有效性与一个真实的用例进行专门研究物种入侵的领域专家。最后，我们讨论了这个框架的推广到其他现实世界中的应用，如社交网络中的信息扩散和通过航空运输的流行病传播。
英文摘要：
Unlike the conventional first-order network (FoN), the higher-order network (HoN) provides a more accurate description of transitions by creating additional nodes to encode higher-order dependencies. However, there exists no visualization and exploration tool for the HoN. For applications such as the development of strategies to control species invasion through global shipping which is known to exhibit higher-order dependencies, the existing FoN visualization tools are limited. In this paper, we present HoNVis, a novel visual analytics framework for exploring higher-order dependencies of the global ocean shipping network. Our framework leverages coordinated multiple views to reveal the network structure at three levels of detail (i.e., the global, local, and individual port levels). Users can quickly identify ports of interest at the global level and specify a port to investigate its higher-order nodes at the individual port level. Investigating a larger-scale impact is enabled through the exploration of HoN at the local level. Using the global ocean shipping network data, we demonstrate the effectiveness of our approach with a real-world use case conducted by domain experts specializing in species invasion. Finally, we discuss the generalizability of this framework to other real-world applications such as information diffusion in social networks and epidemic spreading through air transportation.
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。
