发表机构
Sungkyunkwan University（成均馆大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究针对消息传递图神经网络局限，提出CondPSE编码器，用多项式图滤波器组和条件调制处理节点探针，预训练后冻结用作下游输入编码。在合成基准上提升结构判别准确率，在分子性质预测中与GPSE相当，探讨了其优势及局限性。
AI 中文摘要
消息传递图神经网络受限于1-WL测试，可能会错过区分非同构图的拓扑结构。位置和结构编码（PSE）注入此类拓扑衍生信号，像GPSE这样的学习型PSE编码器预训练单个编码器从随机节点探针生成这些信号，然后可冻结并在下游图模型中复用为输入。我们提出CondPSE，一种学习型PSE编码器，它将可学习的多项式图滤波器组应用于标准高斯节点探针，并通过基于跨滤波器、局部消息传递和图级信号的FiLM风格调制来细化所得的结构响应分支。CondPSE经过预训练以重建节点级位置/结构目标和图级不变量，然后冻结用作下游输入编码。在合成结构判别基准上，CondPSE能区分1-WL受限消息传递无法区分的图结构：相对于GPSE，它将CSL准确率从42.9%提高到97.3%，EXP准确率从68.3%提高到99.9%，消融实验表明多项式滤波器组贡献了大部分增益。在真实分子性质预测中，情况较为有限。使用混合局部消息传递/全局注意力主干，CondPSE与GPSE表现相当但未超越，对ZINC主干的扫描显示两个编码器之间没有一致的排序。我们报告这些结果并讨论为何强大的合成结构判别本身不会为冻结的学习型PSE编码器带来下游优势，包括下游集成的作用以及结构预训练目标与分子性质标签之间可能的不匹配。
英文摘要
Message-passing graph neural networks are bounded by the 1-WL test and can miss topological structure that distinguishes non-isomorphic graphs. Positional and structural encodings (PSE) inject such topology-derived signals, and learned PSE encoders such as GPSE pretrain a single encoder to produce these signals from random node probes, which can then be frozen and reused as inputs across downstream graph models. We present CondPSE, a learned PSE encoder that applies a learnable polynomial graph filter bank to standard Gaussian node probes and refines the resulting structural-response branches through FiLM-style modulation conditioned on cross-filter, local message-passing, and graph-level signals. CondPSE is pretrained to reconstruct node-level positional/structural targets and graph-level invariants, and is then frozen for use as a downstream input encoding. On synthetic structural-discrimination benchmarks, CondPSE separates graph structures that 1-WL-bounded message passing cannot: it raises CSL accuracy from 42.9% to 97.3% and EXP accuracy from 68.3% to 99.9% relative to GPSE, and ablations show that the polynomial filter bank accounts for most of this gain. On real molecular property prediction, the picture is more limited. With a hybrid local-message-passing/global-attention backbone, CondPSE performs comparably to GPSE without surpassing it, and a ZINC backbone sweep shows no consistent ordering between the two encoders. We report these results and discuss why strong synthetic structural discrimination does not, on its own, yield a downstream advantage for frozen learned PSE encoders, including the role of downstream integration and possible mismatch between structural pretraining targets and molecular property labels.
CommentsAccepted as a poster at the 2nd Frontiers in Graph Machine Learning for the Large Model Era (GMLLM'26), a KDD 2026 workshop
