VisGraphVar: A Benchmark Generator for Assessing Variability in Graph Analysis Using Large Vision-Language Models

TL;DR

This research introduces VisGraphVar (Visual Graph Variability), a customizable benchmark generator able to produce graph images for seven distinct task categories and systematically evaluate the strengths and limitations of individual LVLMs, to guide the development of more reliable and robust systems capable of performing advanced visual graph analysis.

Abstract

The fast advancement of Large Vision-Language Models (LVLMs) has shown immense potential. These models are increasingly capable of tackling abstract visual tasks. Geometric structures, particularly graphs with their inherent flexibility and complexity, serve as an excellent benchmark for evaluating these models' predictive capabilities. While human observers can readily identify subtle visual details and perform accurate analyses, our investigation reveals that state-of-the-art LVLMs exhibit consistent limitations in specific visual graph scenarios, especially when confronted with stylistic variations. In response to these challenges, we introduce VisGraphVar (Visual Graph Variability), a customizable benchmark generator able to produce graph images for seven distinct task categories (detection, classification, segmentation, pattern recognition, link prediction, reasoning, matching), designed to systematically evaluate the strengths and limitations of individual LVLMs. We use VisGraphVar to produce 990 graph images and evaluate six LVLMs, employing two distinct prompting strategies, namely zero-shot and chain-of-thought. The findings demonstrate that variations in visual attributes of images (e.g., node labeling and layout) and the deliberate inclusion of visual imperfections, such as overlapping nodes, significantly affect model performance. This research emphasizes the importance of a comprehensive evaluation across graph-related tasks, extending beyond reasoning alone. VisGraphVar offers valuable insights to guide the development of more reliable and robust systems capable of performing advanced visual graph analysis.

Figures (19)

• Figure 1: A general overview of the seven tasks covered by VisGraphVar (1-7), each representing a different challenge for LVLMs, enabling us to conduct a more detailed performance comparison and evaluation.
• Figure 2: Available configurations for generating graph images to evaluate node and edge detection capabilities.
• Figure 3: LVLM execution of Task 1 with overlapping nodes and prompt input.
• Figure 4: Seven different types of graphs.
• Figure 5: Networks with an increasing number of nodes and a single cut-edge: the graph on the left has cut-edge (6, 7); the one in the center has (1, 19); and the most complex one to detect, on the right, has (4, 23).