Graph neural networks (GNNs) are effective machine learning models for various graph learning problems. Despite their empirical successes, the theoretical limitations of GNNs have been revealed recently. Consequently, many GNN models have been proposed to overcome these limitations. In this survey, we provide a comprehensive overview of the expressive power of GNNs and provably powerful variants of GNNs.

翻译：神经网络图(GNN)是解决各种图表学习问题的有效机器学习模式,尽管它们取得了经验性的成功,但最近揭示了GNN的理论局限性,因此提出了许多GNN模型来克服这些局限性,在这次调查中,我们全面概述了GNN和GNN的强大变体的表达力。