Grammatical Path Network: Unveiling Cycles Through Path Computation
Résumé
Graph Neural Networks (GNNs) have demonstrated strong capabilities in learning from structured data. Yet efficiently capturing cycle-related information remains a computational challenge. In this work, we introduce a novel GNN called Grammatical Path Network (GPN) to efficiently capture cycle-related information inside graph structures. GPN leverage Context-Free Grammars (CFGs) for cycle counting through path precomputation. Inspired by the Graph Substructure Network (GSN) framework and recent advances in GNN expressiveness, GPN exploits CFG-based representations to encode cycles of length l + 1 by precomputing paths of length l at the edge level. This formulation eliminates the need for explicit cycle counting, while maintaining strong predictive performance. Our experiments on benchmark datasets demonstrate that GPN achieves comparable or superior results to GSN in tasks requiring cycle-aware representations, highlighting its efficiency and effectiveness. These findings suggest that CFG-guided path precomputation offers a scalable alternative for capturing higher-order structural dependencies in graph-based learning.
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