论文标题
一个集体学习框架，以提高GNN表现力
A Collective Learning Framework to Boost GNN Expressiveness
论文作者
论文摘要
Graph神经网络（GNN）最近已用于取得巨大成功的节点和图形分类任务，但是GNNS模型依赖性在附近相邻节点的属性之间，而不是观察到的节点标签之间的依赖性。在这项工作中，我们考虑了使用GNN在监督和半监督的设置中使用GNN进行归纳性节点分类的任务，目的是结合标签依赖项。由于当前的GNN不是通用的（即最表现的）图表，因此我们提出了一种一般的集体学习方法来增加任何现有GNN的表示能力。我们的框架将集体分类的想法与自我监督的学习结合在一起，并使用蒙特卡洛方法来抽样嵌入，以跨图进行归纳学习。我们评估了五个现实世界网络数据集的性能，并为各种最先进的GNN显示了节点分类精度的一致性，显着提高。
Graph Neural Networks (GNNs) have recently been used for node and graph classification tasks with great success, but GNNs model dependencies among the attributes of nearby neighboring nodes rather than dependencies among observed node labels. In this work, we consider the task of inductive node classification using GNNs in supervised and semi-supervised settings, with the goal of incorporating label dependencies. Because current GNNs are not universal (i.e., most-expressive) graph representations, we propose a general collective learning approach to increase the representation power of any existing GNN. Our framework combines ideas from collective classification with self-supervised learning, and uses a Monte Carlo approach to sampling embeddings for inductive learning across graphs. We evaluate performance on five real-world network datasets and demonstrate consistent, significant improvement in node classification accuracy, for a variety of state-of-the-art GNNs.
