Beyond Message Passing: A Symbolic Alternative for Expressive and Interpretable Graph Learning
arXiv:2602.16947v1 Announce Type: new Abstract: Graph Neural Networks (GNNs) have become essential in high-stakes domains such as drug discovery, yet their black-box nature remains a significant barrier to trustworthiness. While self-explainable GNNs attempt to bridge this gap, they often rely on standard message-passing backbones that inherit fundamental limitations, including the 1-Weisfeiler-Lehman (1-WL) expressivity barrier and a lack of fine-grained interpretability. To address these challenges, we propose SymGraph, a symbolic framework designed to transcend these constraints. By replacing continuous message passing with discrete structural hashing and topological role-based aggregation, our architecture theoretically surpasses the 1-WL barrier, achieving superior expressiveness without the overhead of differentiable optimization. Extensive empirical evaluations demonstrate that SymGraph achieves state-of-the-art performance, outperforming existing self-explainable GNNs. Notably
arXiv:2602.16947v1 Announce Type: new Abstract: Graph Neural Networks (GNNs) have become essential in high-stakes domains such as drug discovery, yet their black-box nature remains a significant barrier to trustworthiness. While self-explainable GNNs attempt to bridge this gap, they often rely on standard message-passing backbones that inherit fundamental limitations, including the 1-Weisfeiler-Lehman (1-WL) expressivity barrier and a lack of fine-grained interpretability. To address these challenges, we propose SymGraph, a symbolic framework designed to transcend these constraints. By replacing continuous message passing with discrete structural hashing and topological role-based aggregation, our architecture theoretically surpasses the 1-WL barrier, achieving superior expressiveness without the overhead of differentiable optimization. Extensive empirical evaluations demonstrate that SymGraph achieves state-of-the-art performance, outperforming existing self-explainable GNNs. Notably, SymGraph delivers 10x to 100x speedups in training time using only CPU execution. Furthermore, SymGraph generates rules with superior semantic granularity compared to existing rule-based methods, offering great potential for scientific discovery and explainable AI.
Executive Summary
This article proposes a novel symbolic framework, SymGraph, to address the limitations of traditional Graph Neural Networks (GNNs) in high-stakes domains like drug discovery. SymGraph replaces continuous message passing with discrete structural hashing and topological role-based aggregation, theoretically surpassing the 1-Weisfeiler-Lehman (1-WL) expressivity barrier. Empirical evaluations demonstrate state-of-the-art performance and significant speedups in training time using only CPU execution. The framework generates rules with superior semantic granularity, offering great potential for scientific discovery and explainable AI. The SymGraph framework has the potential to bridge the trust gap in GNNs and advance the field of graph learning.
Key Points
- ▸ SymGraph is a symbolic framework that addresses the limitations of traditional GNNs in high-stakes domains like drug discovery.
- ▸ SymGraph theoretically surpasses the 1-WL expressivity barrier by replacing continuous message passing with discrete structural hashing and topological role-based aggregation.
- ▸ SymGraph achieves state-of-the-art performance and significant speedups in training time using only CPU execution.
Merits
Strength in Expressiveness
SymGraph's theoretical ability to surpass the 1-WL expressivity barrier makes it a strong candidate for high-stakes applications like drug discovery.
Improved Interpretability
SymGraph's ability to generate rules with superior semantic granularity offers great potential for scientific discovery and explainable AI.
Significant Speedups
SymGraph's CPU-only execution achieves significant speedups in training time, making it a more efficient alternative to traditional GNNs.
Demerits
Potential Overhead in Training
SymGraph's reliance on discrete structural hashing and topological role-based aggregation may introduce additional overhead in training, potentially offsetting the benefits of its improved expressiveness and interpretability.
Limited Scalability
SymGraph's performance and scalability may be limited by its reliance on CPU execution, potentially making it less suitable for very large-scale applications.
Expert Commentary
The article presents a compelling case for the development of SymGraph, a novel symbolic framework that addresses the limitations of traditional GNNs. While its potential benefits are significant, the framework's performance and scalability remain to be fully evaluated. Furthermore, the article highlights the need for more research into explainable AI and graph learning, particularly in high-stakes domains where trustworthiness and transparency are critical. As the field of GNNs continues to evolve, SymGraph has the potential to play a key role in advancing the development of more trustworthy and transparent AI systems.
Recommendations
- ✓ Further research is needed to fully evaluate the performance and scalability of SymGraph in a variety of high-stakes applications.
- ✓ Investigation into the potential applications of SymGraph in other domains, such as social network analysis and recommendation systems, is warranted.
