论文标题
fosr：一阶光谱重新布线，以解决GNNS中的过度重点
FoSR: First-order spectral rewiring for addressing oversquashing in GNNs
论文作者
论文摘要
图形神经网络（GNN）能够通过沿图的边缘传递消息来利用图形数据的结构。尽管这使GNN可以根据图形结构学习功能，但对于某些图形拓扑，它导致信息效率低下，并且一个称为过句的问题。最近，这与图形的曲率和光谱间隙有关。另一方面，将边缘添加到消息响应图中可能会导致越来越相似的节点表示形式和称为超平面的问题。我们提出了一种计算有效的算法，该算法通过基于光谱扩展将边缘添加到图表中，以防止过度争夺。我们将其与关系架构结合在一起，该建筑使GNN保留原始的图形结构，并可以防止过度平滑。我们通过实验发现，我们的算法在几个图形分类任务中胜过现有的图形重新布线方法。
Graph neural networks (GNNs) are able to leverage the structure of graph data by passing messages along the edges of the graph. While this allows GNNs to learn features depending on the graph structure, for certain graph topologies it leads to inefficient information propagation and a problem known as oversquashing. This has recently been linked with the curvature and spectral gap of the graph. On the other hand, adding edges to the message-passing graph can lead to increasingly similar node representations and a problem known as oversmoothing. We propose a computationally efficient algorithm that prevents oversquashing by systematically adding edges to the graph based on spectral expansion. We combine this with a relational architecture, which lets the GNN preserve the original graph structure and provably prevents oversmoothing. We find experimentally that our algorithm outperforms existing graph rewiring methods in several graph classification tasks.
