Mitigating Over-Smoothing and Over-Squashing using Augmentations of Forman-Ricci Curvature

arXiv:2309.09384v3 Announce Type: replace
Abstract: While Graph Neural Networks (GNNs) have been successfully leveraged for learning on graph-structured data across domains, several potential pitfalls have been described recently. Those include the inability to accurately leverage information encoded in long-range connections (over-squashing), as well as difficulties distinguishing the learned representations of nearby nodes with growing network depth (over-smoothing). An effective way to characterize both effects is discrete curvature: Long-range connections that underlie over-squashing effects have low curvature, whereas edges that contribute …

abstract arxiv cs.lg data domains gnns graph graph neural networks information networks neural networks stat.ml structured data type