We developed DyGETViz, a novel framework for effectively visualizing dynamic graphs (DGs) that are ubiquitous across diverse real-world systems. This framework leverages recent advancements in discrete-time dynamic graph (DTDG) models to adeptly handle the temporal dynamics inherent in dynamic graphs. DyGETViz effectively captures both micro- and macro-level structural shifts within these graphs, offering a robust method for representing complex and massive dynamic graphs. The application of DyGETViz extends to a diverse array of domains, including ethology, epidemiology, finance, genetics, linguistics, communication studies, social studies, and international relations. Through its implementation, DyGETViz has revealed or confirmed various critical insights. These include the diversity of content sharing patterns and the degree of specialization within online communities, the chronological evolution of lexicons across decades, and the distinct trajectories exhibited by aging-related and non-related genes. Importantly, DyGETViz enhances the accessibility of scientific findings to non-domain experts by simplifying the complexities of dynamic graphs. Our framework is released as an open-source Python package for use across diverse disciplines. Our work not only addresses the ongoing challenges in visualizing and analyzing DTDG models but also establishes a foundational framework for future investigations into dynamic graph representation and analysis across various disciplines.

翻译：我们开发了DyGETViz这一创新框架，用于有效可视化广泛存在于各类现实世界系统中的动态图。该框架利用离散时间动态图模型的最新进展，巧妙处理动态图中固有的时序动态特性。DyGETViz能有效捕捉动态图中微观与宏观层面的结构演变，为表征复杂且大规模的动态图提供了稳健方法。该框架可应用于行为学、流行病学、金融学、遗传学、语言学、传播学、社会学及国际关系学等多元领域。通过实际应用，DyGETViz已揭示或验证了多项重要发现：包括网络社区中内容共享模式的多样性及专业化程度、跨数十年词典的历时演变规律，以及衰老相关基因与非相关基因表现出的差异化轨迹。值得注意的是，DyGETViz通过简化动态图的复杂性，显著提升了非领域专家对科学发现的可及性。本框架已作为开源Python软件包发布，供多学科领域使用。我们的工作不仅解决了当前离散时间动态图模型可视化与分析中的持续挑战，更为未来跨学科动态图表征与分析研究建立了基础框架。