Discriminative Graph Representation Learning with Distributed Sampling

Graph neural networks (GNNs) have been widely used to accomplish graph classification tasks such as predicting molecular properties and classifying the labels of proteins. Discovering the latent discriminative substructures (e.g., functional groups in molecules) is a vital task to enhance the classification performance. In this paper, this task is addressed as a problem of discriminative graph representation learning. Specifically, a novel node sampling strategy is developed to achieve this goal. To this end, graph-dependent sampling vectors are first learned by a mini-network to exploit various informative substructures on graphs and sample some representative nodes, which could be regarded as performing a distributed sampling on graphs. Then, the sampled nodes are organized together topologically as a subgraph with landing probabilities of random walks. Moreover, a self-adaptive pooling ratio of nodes is obtained via feature smoothness of graphs, eliminating the trouble of manual selection of subgraph size. As a result, these treatments are equivalent to performing the difficult step of down-pooling operation on non-grid graph data. Extensive experiments and ablation studies on multiple benchmark datasets demonstrate the effectiveness and superiority of our proposed approach. Additionally, interpretability studies illustrate the ability of our model to extract discriminative substructures.