MetaHGNIE：异构知识图谱中的元路径诱导超图对比学习
DualHNIE: Dual-Channel Hypergraph Learning for Node Importance Estimation in Heterogeneous Knowledge Graphs
AI总结：
研究异构知识图谱中节点重要性估计问题，提出DualHNIE框架，通过构建高阶知识图谱、引入互补编码器及对比对齐机制进行显式高阶建模和解缠双通道表示学习，实验验证其优于现有方法。
摘要：
在异构知识图谱中估计节点重要性是推荐、搜索和知识决策系统的一个基本问题。然而，大多数现有方法依赖成对消息传递机制，无法捕捉元关系结构诱导的高阶交互。此外，结构拓扑和语义属性通常在统一嵌入空间中纠缠，模糊了它们不同的归纳偏差，限制了学习到的重要性表示的判别能力。为解决这些限制，我们提出DualHNIE，一种用于节点重要性估计的有原则的双通道超图学习框架。DualHNIE首先通过从元路径序列形成类型化超边来构建高阶知识图谱，实现对高阶关系模式的显式建模。然后引入两个互补编码器：一个结构感知超图注意力网络，对元路径诱导的超边进行局部归一化聚合以捕捉局部结构依赖；一个稀疏分块超图变换器，在保持可扩展计算的同时捕捉全局语义交互。我们还设计了一种带有辅助监督的对比对齐机制，确保跨视图一致性同时保留模态特定表示。在多个基准数据集上的大量实验表明，DualHNIE优于现有方法，验证了显式高阶建模和解缠双通道表示学习对异构知识图谱的有效性。代码和数据集可在该https URL获取。
英文摘要：
Estimating node importance in heterogeneous knowledge graphs is a fundamental problem underlying recommendation, search, and knowledge decision systems. However, most existing methods rely on pairwise message passing mechanisms that fail to capture higher-order interactions induced by meta-relational structures. Furthermore, structural topology and semantic attributes are typically entangled within a unified embedding space, which obscures their distinct inductive biases and limits the discriminative capacity of learned importance representations. To address these limitations, we propose DualHNIE, a principled dual-channel hypergraph learning framework for node importance estimation. DualHNIE first constructs a higher-order knowledge graph by forming typed hyperedges from meta-path sequences, enabling explicit modeling of higher-order relational patterns. It then introduces two complementary encoders: a structure-aware hypergraph attention network that performs locally normalized aggregation over meta-path--induced hyperedges to capture localized structural dependencies, and a sparse--chunked hypergraph transformer that captures global semantic interactions while maintaining scalable computation. We further design a contrastive alignment mechanism with auxiliary supervision, ensuring cross-view consistency while preserving modality-specific representation. Extensive experiments on multiple benchmark datasets demonstrate that DualHNIE outperforms state-of-the-art methods, validating the effectiveness of explicit high-order modeling and disentangled dual-channel representation learning for heterogeneous knowledge graphs. Code and datasets are available\footnote[1]{https://github.com/jiawenchen10/DualHNIE}.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
