Graph neural networks are widely used to learn global representations of graphs, which are then used for regression or classification tasks. Typically, the graphs in such data sets are connected, i.e. each training sample consists of a single internally connected graph associated with a global label. However, there is a wide variety of yet unconsidered but application-relevant tasks, where labels are assigned to sets of disjoint graphs, which requires the generation of global representations of disjoint graphs. In this paper, we present a new data set with chemical reactions, which is illustrating this task. Each sample consists of a pair of disjoint molecular graphs and a joint label representing a scalar measure associated with the chemical reaction of the molecules. We show the initial results of graph neural networks that are able to solve the task within a combinatorial subset of the dataset but do not generalize well to the full data set and unseen (sub)graphs.

翻译：图形神经网络被广泛用于学习全球图形的表示,然后用于回归或分类任务。通常,这类数据集中的图表是相连的,即每个培训样本由与全球标签相关的单一内部链接的图形组成。然而,有各种各样尚未考虑但与应用相关的任务,其中标签被分配到脱节图形组,这需要生成脱节图形组。在本文中,我们展示了一套带有化学反应的新数据集,该数据集正在展示这一任务。每个样本包括一对脱节分子图和一个代表与分子化学反应相关的标度测量的组合标签。我们展示了图形神经网络的初步结果,这些网络能够在数据集的组合组合中解答任务,但并未很好地概括到完整的数据集和看不见的(子)图中。