Message passing is the dominant paradigm in Graph Neural Networks (GNNs). The efficiency of message passing, however, can be limited by the topology of the graph. This happens when information is lost during propagation due to being oversquashed when travelling through bottlenecks. To remedy this, recent efforts have focused on graph rewiring techniques, which disconnect the input graph originating from the data and the computational graph, on which message passing is performed. A prominent approach for this is to use discrete graph curvature measures, of which several variants have been proposed, to identify and rewire around bottlenecks, facilitating information propagation. While oversquashing has been demonstrated in synthetic datasets, in this work we reevaluate the performance gains that curvature-based rewiring brings to real-world datasets. We show that in these datasets, edges selected during the rewiring process are not in line with theoretical criteria identifying bottlenecks. This implies they do not necessarily oversquash information during message passing. Subsequently, we demonstrate that SOTA accuracies on these datasets are outliers originating from sweeps of hyperparameters -- both the ones for training and dedicated ones related to the rewiring algorithm -- instead of consistent performance gains. In conclusion, our analysis nuances the effectiveness of curvature-based rewiring in real-world datasets and brings a new perspective on the methods to evaluate GNN accuracy improvements.

使用离散图曲率测量方法重新连接图形可有效改善信息传递过程中的瓶颈问题，然而，在实际数据集中对已有方法进行评估时发现，这些方法并不一定能够提高图神经网络的准确性。因此，本研究提供了对图曲率导向的重连接方法在实际数据集中的效果的深入分析，并对评估图神经网络准确性改善方法的方法提出了新的观点。

GNN中基于曲率重连的效果及超参数的重新评估