论文标题
节点掩蔽：使图神经网络概括和扩展更好
Node Masking: Making Graph Neural Networks Generalize and Scale Better
论文作者
论文摘要
近年来，图形神经网络（GNN）引起了很多兴趣。从只能在每个跨传导性学习范式上只能在无向图上运行的早期光谱体系结构，到可以在任意图中归纳的最新空间状态，GNN从研究界中看到了重要的贡献。在本文中，我们利用一些理论工具更好地想象着最先进的空间GNN执行的操作。我们分析了这些体系结构的内部工作，并引入了一个简单的概念，即节点掩盖，从而使它们可以更好地概括和扩展。为了验证该概念，我们在传播和归纳性环境中对一些广泛使用的数据集进行了几项实验，以进行节点分类，从而为未来的研究奠定了强大的基准。
Graph Neural Networks (GNNs) have received a lot of interest in the recent times. From the early spectral architectures that could only operate on undirected graphs per a transductive learning paradigm to the current state of the art spatial ones that can apply inductively to arbitrary graphs, GNNs have seen significant contributions from the research community. In this paper, we utilize some theoretical tools to better visualize the operations performed by state of the art spatial GNNs. We analyze the inner workings of these architectures and introduce a simple concept, Node Masking, that allows them to generalize and scale better. To empirically validate the concept, we perform several experiments on some widely-used datasets for node classification in both the transductive and inductive settings, hence laying down strong benchmarks for future research.
