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Graph neural networks are widely used to learn global representations ofgraphs, which are then used for regression or classification tasks. Typically,the graphs in such data sets are connected, i.e. each training sample consistsof 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 thegeneration of global representations of disjoint graphs. In this paper, wepresent a new data set with chemical reactions, which is illustrating thistask. Each sample consists of a pair of disjoint molecular graphs and a jointlabel representing a scalar measure associated with the chemical reaction ofthe molecules. We show the initial results of graph neural networks that areable to solve the task within a combinatorial subset of the dataset but do notgeneralize well to the full data set and unseen (sub)graphs.

Graph neural networks to learn joint representations of disjoint molecular graphs