From GNNs to Symbolic Surrogates via Kolmogorov-Arnold Networks for Delay Prediction

This article likely presents a novel approach to delay prediction, potentially in a network or system context. It leverages Graph Neural Networks (GNNs) and transforms them into symbolic surrogates using Kolmogorov-Arnold Networks. The focus is on improving interpretability and potentially efficiency in delay prediction tasks. The use of 'symbolic surrogates' suggests an attempt to create models that are easier to understand and analyze than black-box GNNs.