论文标题
图形神经网络的表达力量的调查
A Survey on The Expressive Power of Graph Neural Networks
论文作者
论文摘要
图形神经网络（GNN）是用于各种图形学习问题的有效机器学习模型。尽管取得了经验成功，但GNN的理论局限性最近已揭示。因此，已经提出了许多GNN模型来克服这些局限性。在这项调查中，我们提供了GNN和GNNS强大变体的表达能力的全面概述。
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.
