Information Visualization (InfoVis) systems utilize visual representations to enhance data interpretation. Understanding how visual attention is allocated is essential for optimizing interface design. However, collecting Eye-tracking (ET) data presents challenges related to cost, privacy, and scalability. Computational models provide alternatives for predicting gaze patterns, thereby advancing InfoVis research. In our study, we conducted an ET experiment with 40 participants who analyzed graphs while responding to questions of varying complexity within the context of digital forensics. We compared human scanpaths with synthetic ones generated by models such as DeepGaze, UMSS, and Gazeformer. Our research evaluates the accuracy of these models and examines how question complexity and number of nodes influence performance. This work contributes to the development of predictive modeling in visual analytics, offering insights that can enhance the design and effectiveness of InfoVis systems.

翻译：信息可视化系统利用视觉表征来增强数据解读能力。理解视觉注意力的分配方式对于优化界面设计至关重要。然而，眼动追踪数据的收集在成本、隐私和可扩展性方面存在挑战。计算模型为预测注视模式提供了替代方案，从而推动了信息可视化研究的发展。在本研究中，我们开展了包含40名参与者的眼动追踪实验，参与者在数字取证背景下分析图结构并回答不同复杂度的问题。我们将人类扫视路径与DeepGaze、UMSS和Gazeformer等模型生成的合成路径进行比较。本研究评估了这些模型的预测准确性，并探讨了问题复杂度与节点数量如何影响模型性能。这项工作推动了可视化分析中预测建模的发展，为提升信息可视化系统的设计效能提供了理论依据。