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.

翻译：尽管在过去几年中取得了快速进展,但最近的研究表明,现代图形神经网络仍然可以在非常简单的任务上失败,比如探测小循环。这暗示了当前网络无法掌握当地结构的信息,如果下游任务严重依赖图形子结构分析(如化学方面),则问题在于当地结构的信息。我们建议对目前标准的GIN演动进行非常简单的修正,使网络能够检测小周期,而计算时间和参数数量几乎不花费成本。通过实际生命分子财产数据集测试,我们的模型不断改进全球和每个任务设置所有基线上大型多任务数据集的性能。