S³GNN mitigates oversquashing in message-passing networks via lightweight global mixing without strong prior assumptions, yielding up to 10x error reduction and 50% fewer parameters across multiple domains.
Title resolution pending
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
cs.LG 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
Linearized Graph Sequence Models recast graph message-passing as sequence modeling via separation of processing depth from propagation depth to integrate modern sequence advances while preserving graph inductive bias.
citing papers explorer
-
S$^3$GNN: Efficient Global Mixing and Local Message Passing for Long-Range Graph Learning
S³GNN mitigates oversquashing in message-passing networks via lightweight global mixing without strong prior assumptions, yielding up to 10x error reduction and 50% fewer parameters across multiple domains.
-
From Message-Passing to Linearized Graph Sequence Models
Linearized Graph Sequence Models recast graph message-passing as sequence modeling via separation of processing depth from propagation depth to integrate modern sequence advances while preserving graph inductive bias.