Message-Passing State-Space Models: Improving Graph Learning with Modern Sequence Modeling

📰 ArXiv cs.AI

Learn how Message-Passing State-Space Models improve graph learning by leveraging modern sequence modeling techniques

advanced Published 27 May 2026
Action Steps
  1. Apply message-passing algorithms to graph-structured data using State-Space Models
  2. Configure Graph State-Space Models (GSSMs) to maintain permutation equivariance and message-passing compatibility
  3. Test GSSMs on benchmark graph datasets to evaluate their performance
  4. Compare the results of GSSMs with existing graph learning models
  5. Run experiments to analyze the computational efficiency of GSSMs
Who Needs to Know This

Researchers and engineers working on graph learning and sequence modeling can benefit from this approach to improve their models' performance and efficiency

Key Insight

💡 Message-Passing State-Space Models can effectively learn graph-structured data while maintaining key properties like permutation equivariance and computational efficiency

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🤖 Improve graph learning with Message-Passing State-Space Models! 📈

Full Article

Title: Message-Passing State-Space Models: Improving Graph Learning with Modern Sequence Modeling

Abstract:
arXiv:2505.18728v2 Announce Type: replace-cross Abstract: The recent success of State-Space Models (SSMs) in sequence modeling has motivated their adaptation to graph learning, giving rise to Graph State-Space Models (GSSMs). However, existing GSSMs operate by applying SSM modules to sequences extracted from graphs, often compromising core properties such as permutation equivariance, message-passing compatibility, and computational efficiency. In this paper, we introduce a new perspective by emb
Read full paper → ← Back to Reads

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