While graph neural networks (GNNs) have become the de-facto standard for graph-based node classification, they impose a strong assumption on the availability of sufficient labeled samples. This assumption restricts the classification performance of prevailing GNNs on many real-world applications suffering from low-data regimes. Specifically, features extracted from scarce labeled nodes could not provide sufficient supervision for the unlabeled samples, leading to severe over-fitting. In this work, we point out that leveraging subgraphs to capture long-range dependencies can augment the representation of a node with homophily properties, thus alleviating the low-data regime. However, prior works leveraging subgraphs fail to capture the long-range dependencies among nodes. To this end, we present a novel self-supervised learning framework, called multi-view subgraph neural networks (Muse), for handling long-range dependencies. In particular, we propose an information theory-based identification mechanism to identify two types of subgraphs from the views of input space and latent space, respectively. The former is to capture the local structure of the graph, while the latter captures the long-range dependencies among nodes. By fusing these two views of subgraphs, the learned representations can preserve the topological properties of the graph at large, including the local structure and long-range dependencies, thus maximizing their expressiveness for downstream node classification tasks. Experimental results show that Muse outperforms the alternative methods on node classification tasks with limited labeled data.

翻译：图神经网络（GNNs）已成为基于图的节点分类事实标准，但它们对充足标注样本的可用性存在强假设。这一假设限制了主流GNN在许多面临低数据场景的真实应用中的分类性能。具体而言，从稀缺标注节点提取的特征无法为未标注样本提供充分监督，导致严重过拟合。本文指出，利用子图捕获长程依赖关系可增强具有同质性特性的节点表示，从而缓解低数据问题。然而，现有基于子图的研究未能有效捕捉节点间的长程依赖。为此，我们提出一种新型自监督学习框架——多视角子图神经网络（Muse），专门处理长程依赖问题。具体而言，我们提出基于信息论的识别机制，分别从输入空间和隐空间视角识别两类子图：前者捕获图的局部结构，后者捕捉节点间的长程依赖。通过融合这两类子图视角，学习到的表示能完整保留图的拓扑属性（包括局部结构与长程依赖），从而最大化其对下游节点分类任务的表达能力。实验结果表明，在标注数据有限的情况下，Muse在节点分类任务上优于对比方法。