用新型谱嵌入提升图神经网络在特征不足时的性能
Beyond the Laplacian: Interpolated Spectral Augmentation for Graph Neural Networks
- 提出插值拉普拉斯嵌入，融合多种图矩阵生成更优特征
- 在多个真实数据集上验证，显著提升GNN分类准确率
- 适合特征稀疏场景的图学习任务，实用性强
图神经网络（GNN）在图机器学习中至关重要，其性能依赖于丰富的节点特征。但在实际数据集中，节点特征常受限或缺失。一种自然方法是利用图拉普拉斯矩阵特征向量计算的嵌入来增强特征。尽管拉普拉斯谱嵌入常见，我们探讨了其他图矩阵的谱嵌入是否也具代表性。本文提出插值拉普拉斯嵌入（ILEs），基于一个简单但表达力强的图矩阵族构造。借助谱图论工具，我们给出了ILEs所捕捉结构信息的直观解释。通过仿真和真实数据集实验，证明使用ILEs进行特征增强能有效提升多种主流GNN架构的性能。该方法为节点特征有限时提供了简洁实用的谱增强新工具。
原文摘要 · Abstract (English)
Graph neural networks (GNNs) are fundamental tools in graph machine learning. The performance of GNNs relies crucially on the availability of informative node features, which can be limited or absent in real-life datasets and applications. A natural remedy is to augment the node features with embeddings computed from eigenvectors of the graph Laplacian matrix. While it is natural to default to Laplacian spectral embeddings, which capture meaningful graph connectivity information, we ask whether spectral embeddings from alternative graph matrices can also provide useful representations for learning. We introduce Interpolated Laplacian Embeddings (ILEs), which are derived from a simple yet expressive family of graph matrices. Using tools from spectral graph theory, we offer a straightforward interpretation of the structural information that ILEs capture. We demonstrate through simulations and experiments on real-world datasets that feature augmentation via ILEs can improve performance across commonly used GNN architectures. Our work offers a straightforward and practical approach that broadens the practitioner's spectral augmentation toolkit when node features are limited.
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