# Can VLMs Assess Similarity Between Graph Visualizations?

arXiv: https://arxiv.org/abs/2504.09859 · HTML: https://mlsanity.com/paper/2504.09859/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Apr 2025.

## Built on by (most cited first, 1 of 1)

- [Disentangling Visual and Factual Correctness in LVLMs' Visualization Literacy](https://mlsanity.com/paper/2606.03142/) (2026)

## Abstract (arXiv)

Graph visualizations have been studied for tasks such as clustering and temporal analysis, but how these visual similarities relate to established graph similarity measures remains unclear. In this paper, we explore the potential of Vision Language Models (VLMs) to approximate human-like perception of graph similarity. We generate graph datasets of various sizes and densities and compare VLM-derived visual similarity scores with feature-based measures. Our findings indicate VLMs can assess graph similarity in a manner similar to feature-based measures, even though differences among the measures exist. In future work, we plan to extend our research by conducting experiments on human visual graph perception.