HyperGCL：通过可学习超图视图实现的多模态图对比学习
HyperGCL: Multi-Modal Graph Contrastive Learning via Learnable Hypergraph Views
AI总结：
本文提出HyperGCL，一种基于超图视角的多模态图对比学习框架，通过可学习自适应拓扑增强构建三种超图视图，并利用网络感知对比损失定义正负样本，在节点分类任务上达到最先进性能。
摘要：
图对比学习（GCL）的最新进展已证明其在改进图表示方面具有显著有效性。然而，依赖预定义的增强方法（如节点丢弃、边扰动、属性掩蔽）可能导致任务相关信息丢失，并缺乏对多样化输入数据的适应性。此外，负样本的选择仍鲜有探索。在本文中，我们从超图视角引入了一种新颖的多模态GCL框架——HyperGCL。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，从而在对比学习中实现多模态的全面整合。一种可学习的自适应拓扑增强技术通过保留重要关系并过滤噪声来增强这些视图。特定于视图的编码器从每个视图中捕获基本特征，而一种网络感知的对比损失则利用底层拓扑有效定义正样本和负样本。在基准数据集上的大量实验表明，HyperGCL实现了最先进的节点分类性能。
英文摘要：
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.
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。
