Graph Neural Networks (GNNs) are powerful tools for learning from graph-structured data, but their effectiveness is often constrained by two critical challenges: oversquashing, where the excessive compression of information from distant nodes results in significant information loss, and oversmoothing, where repeated message-passing iterations homogenize node representations, obscuring meaningful distinctions. These issues, intrinsically linked to the underlying graph structure, hinder information flow and constrain the expressiveness of GNNs. In this survey, we examine graph rewiring techniques, a class of methods designed to address these structural bottlenecks by modifying graph topology to enhance information diffusion. We provide a comprehensive review of state-of-the-art rewiring approaches, delving into their theoretical underpinnings, practical implementations, and performance trade-offs.

翻译：图神经网络（GNNs）是处理图结构数据的强大工具，但其性能常受限于两大关键挑战：过度挤压（即来自远端节点的信息被过度压缩导致显著信息损失）与过度平滑（即重复的消息传递迭代使节点表征趋于同质化，模糊了有意义的区分）。这些问题本质上与底层图结构相关，阻碍了信息流动并限制了GNN的表达能力。本文系统综述了图重布线技术——这类方法通过修改图拓扑结构以增强信息传播，旨在解决上述结构瓶颈。我们对前沿的重布线方法进行了全面梳理，深入探讨其理论基础、实施方案与性能权衡。