研究人员推出了一种名为 Cheeger--Hodge 对比学习 (CHCL) 的新框架，旨在增强图表示学习的鲁棒性。传统方法在面对结构扰动时常常表现不佳，而 CHCL 通过对图数据不同视图中的稳定 Cheeger--Hodge 联合签名进行对齐来解决这一问题。该签名整合了连通性信息和高阶结构细节，从而产生更具韧性的图嵌入。实验表明，CHCL 在各种基准测试中显著提高了性能和泛化能力。 AI
影响 增强了图嵌入的鲁棒性，有望在节点分类和链接预测等下游任务中提高性能。
排序理由 详细介绍图表示学习新方法的学术论文。
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arXiv:2604.27387v1 Announce Type: new Abstract: Heterogeneous graphs with heterophily have emerged as a powerful abstraction for modeling complex real-world systems, where nodes of different types and labels interact in diverse and often non-homophilous ways. Despite recent advan…
arXiv:2604.26301v1 Announce Type: new Abstract: Graph Contrastive Learning (GCL) has emerged as a prominent framework for unsupervised graph representation learning. However, relying on augmentation design alone to define the invariances learned by GCL can be brittle under struct…
Graph Contrastive Learning (GCL) has emerged as a prominent framework for unsupervised graph representation learning. However, relying on augmentation design alone to define the invariances learned by GCL can be brittle under structural perturbations. To address this issue, we pr…