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.

本文提出了一种基于集体学习和自监督学习的蒙特卡洛采样框架，用于将当前的 GNN 模型进行增强以实现节点分类，实验结果表明，该方法在五个真实世界的网络数据集上都取得显著的性能提升。