Graph neural networks (GNNs) are effective machine learning models for many graph-related applications. Despite their empirical success, many research efforts focus on the theoretical limitations of GNNs, i.e., the GNNs expressive power. Early works in this domain mainly focus on studying the graph isomorphism recognition ability of GNNs, and recent works try to leverage the properties such as subgraph counting and connectivity learning to characterize the expressive power of GNNs, which are more practical and closer to real-world. However, no survey papers and open-source repositories comprehensively summarize and discuss models in this important direction. To fill the gap, we conduct a first survey for models for enhancing expressive power under different forms of definition. Concretely, the models are reviewed based on three categories, i.e., Graph feature enhancement, Graph topology enhancement, and GNNs architecture enhancement.

翻译：图神经网络（GNNs）是众多图相关应用中有效的机器学习模型。尽管在实证上取得了成功，许多研究仍聚焦于GNNs的理论局限性，即其表达能力。早期工作主要研究GNNs的图同构识别能力，而近期研究尝试利用子图计数和连通性学习等性质来刻画GNNs的表达能力，这些特性更为实用且贴近实际应用。然而，目前尚无综述论文和开源代码库系统性地总结和讨论这一重要方向中的模型。为填补该空白，我们首次对不同定义形式下增强表达能力模型进行了综述。具体而言，模型基于三类方法进行评述：图特征增强、图拓扑增强以及GNNs架构增强。