  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. Code will be released.
