Abstract reasoning, particularly in the visual domain, is a complex human ability, but it remains a challenging problem for artificial neural learning systems. In this work we propose MXGNet, a multilayer graph neural network for multi-panel diagrammatic reasoning tasks. MXGNet combines three powerful concepts, namely, object-level representation, graph neural networks and multiplex graphs, for solving visual reasoning tasks. MXGNet first extracts object-level representations for each element in all panels of the diagrams, and then forms a multi-layer multiplex graph capturing multiple relations between objects across different diagram panels. MXGNet summarises the multiple graphs extracted from the diagrams of the task, and uses this summarisation to pick the most probable answer from the given candidates. We have tested MXGNet on two types of diagrammatic reasoning tasks, namely Diagram Syllogisms and Raven Progressive Matrices (RPM). For an Euler Diagram Syllogism task MXGNet achieves state-of-the-art accuracy of 99.8%. For PGM and RAVEN, two comprehensive datasets for RPM reasoning, MXGNet outperforms the state-of-the-art models by a considerable margin.

本文提出了一种名为MXGNet的多层图神经网络，用于解决多面板图解推理任务，它通过对象级别表示、图神经网络和多路复用图等三个强大概念，提取图表中元素的对象级别表示，形成捕捉不同图表面板之间对象之间多个关系的多层多路复用图，并从任务提取的多个图表中总结信息以从给定的答案选项中选择最可能的答案，这种方法在欧拉图的演绎推理任务中实现了99.8%的现有最高精度，并且在针对RPM推理的两个全面数据集PGM和RAVEN上，MXGNet的表现超越了现有最先进的模型。

多重图网络在抽象图解推理中的应用