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.

DyGETViz 是一个用于可视化动态图 (DGs) 的新框架，它利用了离散时间动态图 (DTDG) 模型的最新进展，灵活处理动态图中固有的时间动态。DyGETViz 能够有效捕捉图中的微观和宏观结构变化，为表示复杂而庞大的动态图提供了一种强大的方法。通过 DyGETViz 的应用，揭示或证实了各种重要的发现，包括在线社区的内容分享模式和专业化程度的多样性、词汇表在几十年间的时间演化以及与衰老相关和非相关基因展示出的不同轨迹。DyGETViz 通过简化动态图的复杂性，提高了科学发现对非专业人士的可访问性。我们的框架作为一个开源 Python 包发布，可用于各个学科领域。我们的工作不仅解决了可视化和分析 DTDG 模型的持续挑战，还为未来在各个学科领域动态图表示和分析的研究奠定了基础。