用外部干净链接约束隐空间，提升噪声图上的模型性能
Robust Learning on Noisy Graphs via Latent Space Constraints with External Knowledge
- 用外部清洁边与去噪图双编码器约束隐空间
- 在中等噪声图上优于标准及抗噪GNN模型
- 适合关系数据含噪声的场景，提升可解释性
图神经网络在存在噪声边时表现不佳。本文提出隐空间约束图神经网络（LSC-GNN），利用外部提供的‘干净’边来引导有噪声目标图的嵌入表示。训练两个编码器：一个在包含外部边的完整图上训练，另一个在排除目标图潜在噪声边的正则化图上训练，通过惩罚两者隐空间表示的差异，使模型避免过拟合虚假边。在基准数据集上的实验表明，当图存在中等程度噪声时，LSC-GNN优于标准及抗噪型GNN。我们还将该方法扩展至异质图，在小规模蛋白质-代谢物网络上验证，其中代谢物-蛋白质相互作用有效降低了蛋白质共现数据中的噪声。结果表明，该方法能显著提升噪声关系结构下的预测性能与可解释性。
原文摘要 · Abstract (English)
Graph Neural Networks (GNNs) often struggle with noisy edges. We propose Latent Space Constrained Graph Neural Networks (LSC-GNN) to incorporate external "clean" links and guide embeddings of a noisy target graph. We train two encoders--one on the full graph (target plus external edges) and another on a regularization graph excluding the target's potentially noisy links--then penalize discrepancies between their latent representations. This constraint steers the model away from overfitting spurious edges. Experiments on benchmark datasets show LSC-GNN outperforms standard and noise-resilient GNNs in graphs subjected to moderate noise. We extend LSC-GNN to heterogeneous graphs and validate it on a small protein-metabolite network, where metabolite-protein interactions reduce noise in protein co-occurrence data. Our results highlight LSC-GNN's potential to boost predictive performance and interpretability in settings with noisy relational structures.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。