# A Comparative Study of Scanpath Models in Graph-Based Visualization

arXiv: https://arxiv.org/abs/2503.24160 · HTML: https://mlsanity.com/paper/2503.24160/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 56 references that are in the map.
- First posted on arXiv 31 Mar 2025.

## Builds on (most cited first, 13 of 13)

- [CAT2000: A Large Scale Fixation Dataset for Boosting Saliency Research](https://mlsanity.com/paper/1505.03581/) (2015)
- [Gazeformer: Scalable, Effective and Fast Prediction of Goal-Directed Human Attention](https://mlsanity.com/paper/2303.15274/) (2023)
- [PathGAN: Visual Scanpath Prediction with Generative Adversarial Networks](https://mlsanity.com/paper/1809.00567/) (2018)
- [Predicting Visual Importance Across Graphic Design Types](https://mlsanity.com/paper/2008.02912/) (2020)
- [SaltiNet: Scan-path Prediction on 360 Degree Images using Saliency Volumes](https://mlsanity.com/paper/1707.03123/) (2017)
- [TempSAL -- Uncovering Temporal Information for Deep Saliency Prediction](https://mlsanity.com/paper/2301.02315/) (2023)
- [ScanGAN360: A Generative Model of Realistic Scanpaths for 360circ Images](https://mlsanity.com/paper/2103.13922/) (2021)
- [Eyettention: An Attention-based Dual-Sequence Model for Predicting Human Scanpaths during Reading](https://mlsanity.com/paper/2304.10784/) (2023)
- [Synthesizing Human Gaze Feedback for Improved NLP Performance](https://mlsanity.com/paper/2302.05721/) (2023)
- [Trends, Applications, and Challenges in Human Attention Modelling](https://mlsanity.com/paper/2402.18673/) (2024)
- [Chartist: Task-driven Eye Movement Control for Chart Reading](https://mlsanity.com/paper/2502.03575/) (2025)
- [BubbleView: an interface for crowdsourcing image importance maps and tracking visual attention](https://mlsanity.com/paper/1702.05150/) (2017)
- [Impact of Design Decisions in Scanpath Modeling](https://mlsanity.com/paper/2405.08981/) (2024)

## Abstract (arXiv)

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.