从GNN到符号代理：通过Kolmogorov-Arnold网络进行延迟预测
From GNNs to Symbolic Surrogates via Kolmogorov-Arnold Networks for Delay Prediction
AI总结：
本文提出FlowKANet，通过Kolmogorov-Arnold网络替代传统MLP层，实现高效且准确的延迟预测，并通过符号代理模型提升部署透明性。
摘要：
准确预测流量延迟对于优化和管理现代通信网络至关重要。我们研究了三种层次的建模方法。首先，我们实现了带有注意力机制的消息传递异构GNN，建立了强大的神经基线。其次，我们提出了FlowKANet，在其中Kolmogorov-Arnold网络取代了标准MLP层，减少可训练参数的同时保持具有竞争力的预测性能。FlowKANet集成了KAMP-Attn（Kolmogorov-Arnold消息传递与注意力），将KAN操作符直接嵌入到消息传递和注意力计算中。最后，我们通过块状回归将模型转化为符号代理模型，生成闭合形式的方程，消除可训练权重的同时保留图结构依赖性。结果表明，KAN层在效率和准确性之间提供了良好的权衡，并且符号代理强调了轻量级部署和增强透明性的潜力。
英文摘要：
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.
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。
