Accurate prediction of flow delay is essential for optimizing and managing modern communication networks. We investigate three levels of modeling for this task. First, we implement a heterogeneous GNN with attention-based message passing, establishing a strong neural baseline. Second, we propose FlowKANet in which Kolmogorov-Arnold Networks replace standard MLP layers, reducing trainable parameters while maintaining competitive predictive performance. FlowKANet integrates KAMP-Attn (Kolmogorov-Arnold Message Passing with Attention), embedding KAN operators directly into message-passing and attention computation. Finally, we distill the model into symbolic surrogate models using block-wise regression, producing closed-form equations that eliminate trainable weights while preserving graph-structured dependencies. The results show that KAN layers provide a favorable trade-off between efficiency and accuracy and that symbolic surrogates emphasize the potential for lightweight deployment and enhanced transparency.

翻译：流时延的精准预测对于优化和管理现代通信网络至关重要。本文针对该任务研究了三个层次的建模方法。首先，我们实现了一种基于注意力消息传递的异构图神经网络，建立了强大的神经基线模型。其次，我们提出了FlowKANet模型，其中使用Kolmogorov-Arnold网络替代标准多层感知机层，在保持竞争力预测性能的同时减少了可训练参数量。FlowKANet集成了KAMP-Attn（基于注意力的Kolmogorov-Arnold消息传递），将KAN算子直接嵌入到消息传递和注意力计算中。最后，我们通过分块回归将模型蒸馏为符号代理模型，生成闭式方程，在消除可训练权重的同时保留了图结构依赖关系。实验结果表明，KAN层在效率与精度之间提供了有利的权衡，而符号代理模型则凸显了轻量级部署和增强透明度的潜力。