The Underappreciated Power of Vision Models for Graph Structural
  Understanding
Authors: name: Xinjian Zhao, name: Wei Pang, name: Zhongkai Xue, name: Xiangru Jian, name: Lei Zhang, name: Yaoyao Xu, name: Xiaozhuang Song, name: Shu Wu, name: Tianshu Yu

Graph Neural Networks operate through bottom-up message-passing,
fundamentally differing from human visual perception, which intuitively
captures global structures first. We investigate the underappreciated potential
of vision models for graph understanding, finding they achieve performance
comparable to GNNs on established benchmarks while exhibiting distinctly
different learning patterns. These divergent behaviors, combined with
limitations of existing benchmarks that conflate domain features with
topological understanding, motivate our introduction of GraphAbstract. This
benchmark evaluates models' ability to perceive global graph properties as
humans do: recognizing organizational archetypes, detecting symmetry, sensing
connectivity strength, and identifying critical elements. Our results reveal
that vision models significantly outperform GNNs on tasks requiring holistic
structural understanding and maintain generalizability across varying graph
scales, while GNNs struggle with global pattern abstraction and degrade with
increasing graph size. This work demonstrates that vision models possess
remarkable yet underutilized capabilities for graph structural understanding,
particularly for problems requiring global topological awareness and
scale-invariant reasoning. These findings open new avenues to leverage this
underappreciated potential for developing more effective graph foundation
models for tasks dominated by holistic pattern recognition.