AI 中文总结
该研究提出SIGIL框架，通过结构交互图和关系消息传递网络实现图特征的统一表示，可用于全归纳链接预测，还能统一多种图基础模型设计范式。
AI 中文摘要
构建图基础模型的核心障碍是特征空间维度、语义和结构方面的输入异质性，这种异质性限制了图神经网络泛化到具有未见特征空间的新图的能力。我们用SIGIL框架解决可迁移性挑战，该框架可将任何属性图映射到固定维度的统一表示空间。给定一个图，SIGIL将其提升为结构交互图，其中节点是输入特征维度，带权的类型边编码图的多阶连通性间的特征对齐。关系消息传递网络将每个特征维度嵌入到共享空间，把任意维度的原始节点特征转换为可迁移到任何下游图的表示。根据构造，SIGIL对节点、特征维度和标签的置换是等变的。此外，当输入特征是离散关系的独热指示符时，SIGIL会恢复并严格泛化现有的知识图谱推理基础模型。在单个图上预训练的单个SIGIL模型可实现强大的全归纳链接预测，且SIGIL可用于实现现有的知识图谱基础模型，因此SIGIL在单一框架下统一了图基础模型设计中的若干现有范式。
英文摘要
A central obstacle in building graph foundation models is the input heterogeneity in terms of feature space dimensionality, semantics, and structure. Such heterogeneity limits the capability of graph neural networks to generalize to new graphs with unseen feature spaces. We address the transferability challenge with SIGIL, a framework that maps any attributed graph to a unified representation space of fixed dimension. Given a graph, SIGIL lifts it to a structural interaction graph, where nodes are the input feature dimensions and weighted, typed edges encode feature alignment across multiple orders of the graph's connectivity. A relational message-passing network embeds each feature dimension into a shared space, transforming the original node features, of arbitrary dimensionality, into representations transferable to any downstream graph. By construction, SIGIL is equivariant to permutations of nodes, feature dimensions, and labels. Additionally, when the input features are one-hot indicators of discrete relations, SIGIL recovers and strictly generalizes existing foundation models for knowledge graph reasoning. A single SIGIL model, pretrained on one graph, delivers strong fully-inductive link prediction. Also, SIGIL can be used to implement existing knowledge graph foundation models. As such, SIGIL unifies several existing regimes in graph foundation model design under a single framework
Comments7 pages, 1 figure in the main body. 12 pages, 5 figures in appendix. Submitted to AAAI 2027 (main track) and currently under review
