arXiv 2505.043402025-05-07cs.LGcs.AI

基于多粒度注意力的异构超图神经网络

Multi-Granular Attention based Heterogeneous Hypergraph Neural Network

School of Computer Science, Hubei University（湖北大学计算机学院）；Hubei Key Laboratory of Big Data Intelligent Analysis and Application (Hubei University)（湖北大学大数据智能分析与应用重点实验室）；Discipline of Business Analytics, The University of Sydney（悉尼大学商业分析学科）；Key Laboratory of Intelligent Sensing System and Security (Hubei University), Ministry of Education（智能感知系统与安全重点实验室（湖北大学），教育部）

Hong Jin, Kaicheng Zhou, Jie Yin, Lan You, Zhifeng Zhou

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AI总结：

本文提出MGA-HHN，通过构建基于元路径的异构超图和多粒度注意力机制，解决异构图神经网络中高阶关系缺失与长距离消息失真问题，在节点分类、聚类和可视化任务上取得领先性能。

摘要：

异构图神经网络（HeteGNNs）通过有效提取异构图中复杂的结构和语义信息，展现了强大的节点表示学习能力。现有的大多数HeteGNNs遵循邻域聚合范式，利用基于元路径的消息传递来学习潜在节点表示。然而，由于元路径的成对特性，这些模型无法捕获节点之间的高阶关系，导致性能欠佳。此外，“过度压缩”问题——即HeteGNNs中的长距离消息传递导致严重的信息失真——进一步限制了这些模型的有效性。为了解决这些局限性，本文提出了MGA-HHN，一种基于多粒度注意力的异构超图神经网络，用于异构图表示学习。MGA-HHN引入了两项关键创新：（1）一种构建基于元路径的异构超图的新方法，通过多视图显式建模异构图中的高阶语义信息；（2）一种在节点和超边两个层面上运作的多粒度注意力机制。该机制使模型能够捕获同一超边类型内共享相同语义上下文的节点之间的细粒度交互，同时保留不同超边类型之间语义的多样性。因此，MGA-HHN有效缓解了长距离消息失真，并生成更具表达力的节点表示。在真实世界基准数据集上的大量实验表明，MGA-HHN优于最先进的模型，展示了其在节点分类、节点聚类和可视化任务中的有效性。

英文摘要：

Heterogeneous graph neural networks (HeteGNNs) have demonstrated strong abilities to learn node representations by effectively extracting complex structural and semantic information in heterogeneous graphs. Most of the prevailing HeteGNNs follow the neighborhood aggregation paradigm, leveraging meta-path based message passing to learn latent node representations. However, due to the pairwise nature of meta-paths, these models fail to capture high-order relations among nodes, resulting in suboptimal performance. Additionally, the challenge of ``over-squashing'', where long-range message passing in HeteGNNs leads to severe information distortion, further limits the efficacy of these models. To address these limitations, this paper proposes MGA-HHN, a Multi-Granular Attention based Heterogeneous Hypergraph Neural Network for heterogeneous graph representation learning. MGA-HHN introduces two key innovations: (1) a novel approach for constructing meta-path based heterogeneous hypergraphs that explicitly models higher-order semantic information in heterogeneous graphs through multiple views, and (2) a multi-granular attention mechanism that operates at both the node and hyperedge levels. This mechanism enables the model to capture fine-grained interactions among nodes sharing the same semantic context within a hyperedge type, while preserving the diversity of semantics across different hyperedge types. As such, MGA-HHN effectively mitigates long-range message distortion and generates more expressive node representations. Extensive experiments on real-world benchmark datasets demonstrate that MGA-HHN outperforms state-of-the-art models, showcasing its effectiveness in node classification, node clustering and visualization tasks.

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