用可学习的超图视图提升多模态图对比学习效果
HyperGCL: Multi-Modal Graph Contrastive Learning via Learnable Hypergraph Views
- 通过结构与属性联合构建三种超图视图，融合多模态信息
- 在多个基准数据集上达到当前最优节点分类性能
- 适合关注图神经网络表示学习与多模态融合的研究者
图对比学习（GCL）近年来在提升图表示方面展现出显著效果。然而，依赖预定义的增强方法（如节点删除、边扰动、属性掩码）可能导致任务相关信息丢失，且难以适应多样化的输入数据。此外，负样本的选择问题尚未得到充分探索。本文提出HyperGCL，一种从超图视角出发的新型多模态图对比学习框架。HyperGCL通过联合利用输入图的结构和属性，构建三种不同的超图视图，实现多模态信息的全面融合。引入可学习的自适应拓扑增强技术，在保留重要关系的同时过滤噪声。各视图专用编码器捕捉其关键特征，而网络感知的对比损失则基于底层拓扑有效定义正负样本。在多个基准数据集上的大量实验表明，HyperGCL实现了领先的节点分类性能。
原文摘要 · Abstract (English)
Recent advancements in Graph Contrastive Learning (GCL) have demonstrated remarkable effectiveness in improving graph representations. However, relying on predefined augmentations (e.g., node dropping, edge perturbation, attribute masking) may result in the loss of task-relevant information and a lack of adaptability to diverse input data. Furthermore, the selection of negative samples remains rarely explored. In this paper, we introduce HyperGCL, a novel multimodal GCL framework from a hypergraph perspective. HyperGCL constructs three distinct hypergraph views by jointly utilizing the input graph's structure and attributes, enabling a comprehensive integration of multiple modalities in contrastive learning. A learnable adaptive topology augmentation technique enhances these views by preserving important relations and filtering out noise. View-specific encoders capture essential characteristics from each view, while a network-aware contrastive loss leverages the underlying topology to define positive and negative samples effectively. Extensive experiments on benchmark datasets demonstrate that HyperGCL achieves state-of-the-art node classification performance.
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