系统梳理图神经网络泛化能力研究现状
Survey on Generalization Theory for Graph Neural Networks
- 综述现有图神经网络泛化理论研究
- 分析不同方法的优劣与适用条件
- 适合关注图学习理论的科研人员
消息传递图神经网络（MPNNs）已成为图上机器学习的主流方法，近年来受到广泛关注。尽管大量研究探讨了MPNN的表达能力（即区分图和近似图函数的能力），但对其泛化能力（即在训练数据之外做出有意义预测的能力）的研究相对较少。本文系统回顾了现有关于MPNN泛化能力的文献，分析了各研究方法的优势与局限性，提供了对方法论和发现的深入见解。此外，还指出了未来研究的潜在方向，旨在深化对MPNN泛化能力的理解。
原文摘要 · Abstract (English)
Message-passing graph neural networks (MPNNs) have emerged as the leading approach for machine learning on graphs, attracting significant attention in recent years. While a large set of works explored the expressivity of MPNNs, i.e., their ability to separate graphs and approximate functions over them, comparatively less attention has been directed toward investigating their generalization abilities, i.e., making meaningful predictions beyond the training data. Here, we systematically review the existing literature on the generalization abilities of MPNNs. We analyze the strengths and limitations of various studies in these domains, providing insights into their methodologies and findings. Furthermore, we identify potential avenues for future research, aiming to deepen our understanding of the generalization abilities of MPNNs.
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