While the celebrated graph neural networks yield effective representations for individual nodes of a graph, there has been relatively less success in extending to the task of graph similarity learning. Recent work on graph similarity learning has considered either global-level graph-graph interactions or low-level node-node interactions, however ignoring the rich cross-level interactions (e.g., between each node of one graph and the other whole graph). In this paper, we propose a multi-level graph matching network (MGMN) framework for computing the graph similarity between any pair of graph-structured objects in an end-to-end fashion. In particular, the proposed MGMN consists of a node-graph matching network for effectively learning cross-level interactions between each node of one graph and the other whole graph, and a siamese graph neural network to learn global-level interactions between two input graphs. Furthermore, to compensate for the lack of standard benchmark datasets, we have created and collected a set of datasets for both the graph-graph classification and graph-graph regression tasks with different sizes in order to evaluate the effectiveness and robustness of our models. Comprehensive experiments demonstrate that MGMN consistently outperforms state-of-the-art baseline models on both the graph-graph classification and graph-graph regression tasks. Compared with previous work, MGMN also exhibits stronger robustness as the sizes of the two input graphs increase.

翻译：虽然著名的图形神经网络为图表的单个节点提供了有效的表示,但在扩展图类相似性的任务方面,相对而言,在扩展图类相似性学习任务方面,成功率相对较低。最近关于图类相似性学习的工作考虑了全球一级的图形-图形互动或低水平节点-节点-节点互动,但忽视了丰富的跨层次互动(例如,一个图的每个节点与另一个整图之间)。在本文件中,我们提议了一个多层次的图形匹配网络(MGMN)框架,用于计算图表类和图表类结构对象之间任何一对一对一对一对一对一对一对一对一对一对一对一对一对一对一对一对一对一对一对一对一的图形。特别是,拟议的图类图类相似性学习一个点和另一整图类的跨层次互动,而忽视了丰富的跨层次互动。此外,为了弥补标准基准数据集数据集,我们创建并收集了一组图表类比和图表回归性回归任务,以便评估一个图类对比模型的有效性和稳健性。