用KAN网络提升流量延迟预测效率，还能生成可解释的公式。
From GNNs to Symbolic Surrogates via Kolmogorov-Arnold Networks for Delay Prediction
- 用柯尔莫哥洛夫-阿诺德网络替代传统神经网络层，减少参数量
- 模型在保持高精度的同时，参数量大幅降低，推理更高效
- 可生成带图结构依赖的显式数学公式，适合部署与解释
准确预测流量延迟对优化现代通信网络至关重要。本文研究了三个层次的建模方法：首先构建基于注意力的消息传递异构GNN，建立强神经基线；其次提出FlowKANet，用柯尔莫哥洛夫-阿诺德网络（KAN）替换标准MLP层，在减少可训练参数的同时保持竞争力的预测性能；该模型融合了KAMP-Attn（带注意力的柯尔莫哥洛夫-阿诺德消息传递），将KAN算子直接嵌入消息传递和注意力计算中；最后通过分块回归进行知识蒸馏，生成无权重的符号化代理模型，得到闭式表达式，保留图结构依赖关系。结果表明，KAN层在效率与精度间取得良好平衡，符号化代理模型展现出轻量化部署与更高透明度的潜力。
原文摘要 · Abstract (English)
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.
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