基于离散几何的图神经网络重构
Rewiring Networks for Graph Neural Network Training Using Discrete Geometry
摘要：
信息过度挤压是指网络中相距较远的节点之间信息传播效率低下的现象。这是一个重要的问题，众所周知，显着影响图神经网络（GNN）的训练，因为节点的感受野呈指数增长。为了缓解这个问题，通常对输入网络应用称为重新布线的预处理过程。在本文中，我们研究了使用离散类似物的经典几何概念的曲率来模拟网络上的信息流和重新布线。我们证明了这些经典概念在各种真实网络数据集上实现了GNN训练精度的最新性能。此外，与当前最先进的技术相比，这些经典的概念在计算运行时间上表现出几个数量级的明显优势。
英文摘要：
Information over-squashing is a phenomenon of inefficient information propagation between distant nodes on networks. It is an important problem that is known to significantly impact the training of graph neural networks (GNNs), as the receptive field of a node grows exponentially. To mitigate this problem, a preprocessing procedure known as rewiring is often applied to the input network. In this paper, we investigate the use of discrete analogues of classical geometric notions of curvature to model information flow on networks and rewire them. We show that these classical notions achieve state-of-the-art performance in GNN training accuracy on a variety of real-world network datasets. Moreover, compared to the current state-of-the-art, these classical notions exhibit a clear advantage in computational runtime by several orders of magnitude.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
