The Underappreciated Power of Vision Models for Graph Structural
  Understanding
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.
69096b57812eca10f9cc6185
Xinjian Zhao
claimed_verified
2025-11-04T20:26:45.587Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/xp34hfiSLf-DiE1DVVhHk.png
Xinjian Zhao
Xinjiansz
user
Xinjiansz
69096b57812eca10f9cc6186
Wei Pang
claimed_verified
2025-11-04T20:26:47.791Z
65164444bc0631719873af81
/avatars/dab8b90db8bbd00806268fe276e3ea36.svg
Wei Pang
weipang142857
user
weipang142857
69096b57812eca10f9cc6187
Zhongkai Xue
69096b57812eca10f9cc6188
Xiangru Jian
claimed_verified
2025-11-04T20:26:49.963Z
636865b8cca0a0a962c21f3f
https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/Mja7cpws4gb2Jmdj_foPA.png
Xiangru (Edward) Jian
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user
HideOnBush
69096b57812eca10f9cc6189
Lei Zhang
69096b57812eca10f9cc618a
Yaoyao Xu
69096b57812eca10f9cc618b
Xiaozhuang Song
69096b57812eca10f9cc618c
Shu Wu
69096b57812eca10f9cc618d
Tianshu Yu
The Underappreciated Power of Vision Models for Graph Structural
  Understanding
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.
Vision models outperform Graph Neural Networks on tasks requiring global structural understanding and scale-invariant reasoning, as demonstrated by the new GraphAbstract benchmark.
Graph Neural Networks message-passing vision models GraphAbstract global graph properties organizational archetypes symmetry connectivity strength critical elements holistic structural understanding scale-invariant reasoning