摘要
      这项工作考虑了属性关系图（ARG）上的表示学习任务。ARG中的节点和边都与属性/特征相关联，使ARG能够编码在实际应用中广泛观察到的丰富的结构信息。现有的图神经网络在局部结构上下文中捕捉复杂相互作用的能力有限，这阻碍了它们利用ARGs的表达能力。我们提出了基序卷积模块（MCM），这是一种新的基于基序的图表示学习技术，可以更好地利用局部结构信息。处理连续边缘和节点特征的能力是MCM相对于现有的基于基序的模型的优势之一。MCM以无监督的方式构建基序词汇表，并部署一种新的基序卷积运算来提取单个节点的局部结构上下文，然后通过多层感知器和/或图神经网络中的消息传递来学习更高级别的节点表示。与其他对合成图进行分类的图学习方法相比，我们的方法在捕捉结构上下文方面要好得多。我们还通过将其应用于几个分子基准，展示了我们方法的性能和可解释性优势。
Abstract
This work considers the task of representation learning on the attributed relational graph (ARG). Both the nodes and edges in an ARG are associated with attributes/features allowing ARGs to encode rich structural information widely observed in real applications. Existing graph neural networks offer limited ability to capture complex interactions within local structural contexts, which hinders them from taking advantage of the expression power of ARGs. We propose motif convolution module (MCM), a new motif-based graph representation learning technique to better utilize local structural information. The ability to handle continuous edge and node features is one of MCM’s advantages over existing motif-based models. MCM builds a motif vocabulary in an unsupervised way and deploys a novel motif convolution operation to extract the local structural context of individual nodes, which is then used to learn higher level node representations via multilayer perceptron and/or message passing in graph neural networks. When compared with other graph learning approaches to classifying synthetic graphs, our approach is substantially better at capturing structural context. We also demonstrate the performance and explainability advantages of our approach by applying it to several molecular benchmarks.

https://www.mdpi.com/2227-9709/10/1/8