面向带属性标签图学习的SLM条件分层关系路由

中文摘要 AI 辅助

带属性标签图将关系结构与附加在节点和关系上的异构文本及分类属性相结合。传统图神经网络通常将这些属性表示为静态特征向量，限制了其确定哪些语义证据应影响特定预测目标的消息传播的能力。我们提出SLM条件分层关系路由架构，该架构将小型语言模型直接集成到图消息选择中。拓扑GNN提供稳定的结构表示和预测锚点。对于每个目标节点，入射消息结合邻居的结构状态、节点属性编码、关系属性编码和关系类型。参数高效的SLM处理结构化图软标记并生成目标条件路由查询。该查询首先在每种关系类型内选择相关消息，随后跨关系级摘要路由信息。所得表示对拓扑锚点提供有界残差更新，保留结构证据的同时允许上下文语义信息修改预测。该架构支持在邻居和关系类型层面进行可解释分析，并提供将语言衍生语义集成到属性丰富图学习中的通用机制。

英文摘要

Labeled property graphs combine relational structure with heterogeneous textual and categorical properties attached to both nodes and relationships. Conventional graph neural networks typically represent these properties as static feature vectors, limiting their ability to determine which semantic evidence should influence message propagation for a particular prediction target. We propose SLM-Conditioned Hierarchical Relation Routing, an architecture that integrates a small language model directly into graph message selection. A topology GNN provides a stable structural representation and prediction anchor. For each target node, incident messages combine the neighbor's structural state, node-property encoding, relationship-property encoding, and relationship type. A parameter-efficient SLM processes structured graph soft tokens and produces a target-conditioned routing query. This query first selects relevant messages within each relationship type and subsequently routes information across relation-level summaries. The resulting representation provides a bounded residual update to the topology anchor, preserving structural evidence while allowing contextual semantic information to modify the prediction. The architecture supports interpretable analysis at both the neighbor and relationship-type levels and provides a general mechanism for integrating language-derived semantics into property-rich graph learning.

发表机构

• NASK National Research Institute（NASK国家研究院）

机构由 AI 辅助整理，请以论文原文为准。