Graph neural networks (GNNs) have achieved state-of-the-art performance in
graph representation learning. Message passing neural networks, which learn
representations through recursively aggregating information from each node and
its neighbors, are among the most commonly-used GNNs. However, a wealth of
structural information of individual nodes and full graphs is often ignored in
such process, which restricts the expressive power of GNNs. Various graph data
augmentation methods that enable the message passing with richer structure
knowledge have been introduced as one main way to tackle this issue, but they
are often focused on individual structure features and difficult to scale up
with more structure features. In this work we propose a novel approach, namely
collective structure knowledge-augmented graph neural network (CoS-GNN), in
which a new message passing method is introduced to allow GNNs to harness a
diverse set of node- and graph-level structure features, together with original
node features/attributes, in augmented graphs. In doing so, our approach
largely improves the structural knowledge modeling of GNNs in both node and
graph levels, resulting in substantially improved graph representations. This
is justified by extensive empirical results where CoS-GNN outperforms
state-of-the-art models in various graph-level learning tasks, including graph
classification, anomaly detection, and out-of-distribution generalization.

本篇研究提出了一种新颖的方法，即综合结构知识增强图神经网络 (CoS-GNN)，通过引入新的消息传递方法，使得图神经网络能够利用丰富的节点和图级结构特征以及原始节点特征，在增强的图结构中大大改善了图神经网络的结构知识建模，从而实现了显著改进的图表示。在大量实证结果中，CoS-GNN 在包括图分类、异常检测和超出分布泛化在内的各种图级学习任务中优于现有的模型。