Beyond Message Passing: A Symbolic Alternative for Expressive and Interpretable Graph Learning

Current GNNs lack transparency in high-stakes applications: The paper identifies that Graph Neural Networks, widely used in critical domains like drug discovery, operate as "black boxes," making it difficult to understand how they reach decisions—a significant barrier to trustworthiness in responsible AI deployment.

Existing explainability approaches have fundamental technical limitations: Self-explainable GNNs attempt to address transparency but are constrained by the "1-Weisfeiler-Lehman expressivity barrier," meaning they cannot distinguish between certain graph structures, potentially missing important patterns that could affect decision quality.

A symbolic alternative offers both better performance and interpretability: SymGraph replaces traditional continuous message-passing with discrete structural hashing and rule-based reasoning, achieving superior expressiveness while generating human-readable rules with "superior semantic granularity"—enabling scientists to understand why the system makes specific predictions.

Interpretability doesn't require sacrificing efficiency: SymGraph delivers "10x to 100x speedups in training time" while running on standard CPUs, demonstrating that explainable AI systems can be more practical and resource-efficient than existing approaches, potentially making responsible AI more accessible.

Rule-based outputs enable scientific discovery: By generating interpretable rules rather than opaque numerical outputs, SymGraph offers "great potential for scientific discovery," allowing domain experts in drug discovery and other fields to validate findings and build trust in AI recommendations.