Despite quick progress in the last few years, recent studies have shown that modern graph neural networks can still fail at very simple tasks, like detecting small cycles. This hints at the fact that current networks fail to catch information about the local structure, which is problematic if the downstream task heavily relies on graph substructure analysis, as in the context of chemistry. We propose a very simple correction to the now standard GIN convolution that enables the network to detect small cycles with nearly no cost in terms of computation time and number of parameters. Tested on real life molecule property datasets, our model consistently improves performance on large multi-tasked datasets over all baselines, both globally and on a per-task setting.

中文翻译：

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图卷积最终可以对局部结构建模

尽管最近几年取得了长足的进步，但最近的研究表明，现代图神经网络仍会在非常简单的任务（例如检测小周期）上失败。这暗示了这样一个事实，即当前的网络无法捕获有关局部结构的信息，如果下游任务在很大程度上依赖于图的子结构分析（例如在化学环境中），这将是一个问题。我们建议对现在的标准GIN卷积进行非常简单的修正，使网络能够在计算时间和参数数量方面几乎没有成本地检测出小的周期。经过对现实生活中分子特性数据集的测试，我们的模型在全局和按任务设置的基础上，持续提高了所有基线上大型多任务数据集的性能。