Graph representation learning has attracted increasing research attention. However, most existing studies fuse all structural features and node attributes to provide an overarching view of graphs, neglecting finer substructures' semantics, and suffering from interpretation enigmas. This paper presents a novel hierarchical subgraph-level selection and embedding based graph neural network for graph classification, namely SUGAR, to learn more discriminative subgraph representations and respond in an explanatory way. SUGAR reconstructs a sketched graph by extracting striking subgraphs as the representative part of the original graph to reveal subgraph-level patterns. To adaptively select striking subgraphs without prior knowledge, we develop a reinforcement pooling mechanism, which improves the generalization ability of the model. To differentiate subgraph representations among graphs, we present a self-supervised mutual information mechanism to encourage subgraph embedding to be mindful of the global graph structural properties by maximizing their mutual information. Extensive experiments on six typical bioinformatics datasets demonstrate a significant and consistent improvement in model quality with competitive performance and interpretability.
翻译:然而,大多数现有研究结合了所有结构特征和节点属性,以提供图表的总体视图,忽略了精细子结构的语义学,并受到解释谜题的影响。本文件介绍了一个新的等级分层分层选择和嵌入基于图形的图形神经网络,以图解分类,即SUGAR,以学习更具歧视性的子图示,并以解释性的方式作出反应。SUGAR通过提取突出的子图解作为原始图表中揭示子图层的代表性部分,重新绘制了一张草图。在未经事先了解的情况下,我们为适应性地选择了突出的子图谱,我们开发了一个强化集合机制,提高了模型的一般化能力。为了区分图层之间的子图解,我们提出了一个自我监督的相互信息机制,鼓励子谱嵌入全球图形结构特性,最大限度地增加它们的相互信息。对六种典型的生物信息数据集进行了广泛的实验,表明模型质量有了显著和一致的改进,具有竞争性的性能和可解释性能。