Multi-view Fuzzy Graph Attention Networks for Enhanced Graph Learning

• URL: http://arxiv.org/abs/2412.17271v1
• Date: Mon, 23 Dec 2024 04:39:08 GMT
• Title: Multi-view Fuzzy Graph Attention Networks for Enhanced Graph Learning
• Authors: Jinming Xing, Dongwen Luo, Qisen Cheng, Chang Xue, Ruilin Xing,
• Abstract summary: Fuzzy Graph Attention Network (FGAT) has shown promise in tasks requiring robust graph-based learning. We propose the Multi-view Fuzzy Graph Attention Network (MFGAT), a novel framework that constructs and aggregates multi-view information.
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• Abstract: 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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