多视角模糊图注意力网络提升复杂图数据建模能力
Multi-view Fuzzy Graph Attention Networks for Enhanced Graph Learning
- 设计变换模块从多角度动态提取数据特征
- 在多个图分类数据集上超越现有最优模型
- 适合需要细粒度图结构理解的研究场景
模糊图注意力网络（FGAT）结合模糊粗糙集与图注意力机制，在需鲁棒图学习的任务中展现出潜力。然而，现有模型难以有效捕捉多视角依赖关系，限制了对复杂数据的建模能力。为此，本文提出多视角模糊图注意力网络（MFGAT），通过专门设计的变换模块构建并聚合多视图信息。该模块动态转换多维度数据，并以加权求和方式融合表征，实现全面的多视图建模。融合后的信息输入FGAT，增强模糊图卷积效果。此外，引入可学习的全局池化机制，提升图级理解能力。大量实验表明，MFGAT在图分类任务中优于当前最先进基线，验证了其有效性与通用性。
原文摘要 · Abstract (English)
Fuzzy Graph Attention Network (FGAT), which combines Fuzzy Rough Sets and Graph Attention Networks, has shown promise in tasks requiring robust graph-based learning. However, existing models struggle to effectively capture dependencies from multiple perspectives, limiting their ability to model complex data. To address this gap, we propose the Multi-view Fuzzy Graph Attention Network (MFGAT), a novel framework that constructs and aggregates multi-view information using a specially designed Transformation Block. This block dynamically transforms data from multiple aspects and aggregates the resulting representations via a weighted sum mechanism, enabling comprehensive multi-view modeling. The aggregated information is fed into FGAT to enhance fuzzy graph convolutions. Additionally, we introduce a simple yet effective learnable global pooling mechanism for improved graph-level understanding. Extensive experiments on graph classification tasks demonstrate that MFGAT outperforms state-of-the-art baselines, underscoring its effectiveness and versatility.
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