The crux of graph classification lies in the effective representation learning for the entire graph. Typical graph neural networks focus on modeling the local dependencies when aggregating features of neighboring nodes, and obtain the representation for the entire graph by aggregating node features. Such methods have two potential limitations: 1) the global node saliency w.r.t. graph classification is not explicitly modeled, which is crucial since different nodes may have different semantic relevance to graph classification; 2) the graph representation directly aggregated from node features may have limited effectiveness to reflect graph-level information. In this work, we propose the Saliency-Aware Regularized Graph Neural Network (SAR-GNN) for graph classification, which consists of two core modules: 1) a traditional graph neural network serving as the backbone for learning node features and 2) the Graph Neural Memory designed to distill a compact graph representation from node features of the backbone. We first estimate the global node saliency by measuring the semantic similarity between the compact graph representation and node features. Then the learned saliency distribution is leveraged to regularize the neighborhood aggregation of the backbone, which facilitates the message passing of features for salient nodes and suppresses the less relevant nodes. Thus, our model can learn more effective graph representation. We demonstrate the merits of SAR-GNN by extensive experiments on seven datasets across various types of graph data.

图分类的关键在于整个图的有效表示学习。典型的图神经网络侧重于在聚合相邻节点特征时对局部依赖关系进行建模，并通过聚合节点特征获得整个图的表示。这种方法有两个潜在的局限性：1）图分类的全局节点显着性没有明确建模，这一点至关重要，因为不同的节点可能与图分类有不同的语义相关性；2）从节点特征直接聚合的图表示对于反映图级信息的有效性可能有限。在这项工作中，我们提出了用于图分类的显着性感知正则化图神经网络（SAR-GNN），它由两个核心模块组成：1）作为学习节点特征的骨干的传统图神经网络和2）图神经记忆旨在从主干的节点特征中提取紧凑的图形表示。我们首先通过测量紧凑图表示和节点特征之间的语义相似性来估计全局节点显着性。然后利用学习到的显着性分布来规范骨干网的邻域聚合，这有利于显着节点特征的消息传递并抑制不太相关的节点。因此，我们的模型可以学习更有效的图表示。我们通过对不同类型图数据的七个数据集进行广泛实验，展示了SAR-GNN的优点