This paper focuses on the discrimination capacity of aggregation functions: these are the permutation invariant functions used by graph neural networks to combine the features of nodes. Realizing that the most powerful aggregation functions suffer from a dimensionality curse, we consider a restricted setting. In particular, we show that the standard sum and a novel histogram-based function have the capacity to discriminate between any fixed number of inputs chosen by an adversary. Based on our insights, we design a graph neural network aiming, not to maximize discrimination capacity, but to learn discriminative graph representations that generalize well. Our empirical evaluation provides evidence that our choices can yield benefits to the problem of structural graph classification.

翻译：本文侧重于聚合功能的区别能力:这些是图形神经网络用来结合节点特征的变异功能。认识到最强大的集合功能受到一个维度诅咒的影响,我们考虑一个限制的设置。特别是,我们证明标准总和和基于直方图的新功能有能力区分对手所选择的任何固定数量的投入。根据我们的见解,我们设计了一个图形神经网络,目的不是要最大限度地扩大歧视能力,而是要学习能够概括化的歧视性图形表达方式。我们的经验评估提供了证据,证明我们的选择可以对结构图表分类问题产生益处。