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

• URL: http://arxiv.org/abs/2512.20885v1
• Date: Wed, 24 Dec 2025 02:05:46 GMT
• Title: From GNNs to Symbolic Surrogates via Kolmogorov-Arnold Networks for Delay Prediction
• Authors: Sami Marouani, Kamal Singh, Baptiste Jeudy, Amaury Habrard,
• Abstract summary: We implement a heterogeneous GNN with attention-based message passing, establishing a strong neural baseline.<n>Second, we propose FlowKANet in which Kolmogorov-Arnold Networks replace standard layers, reducing trainable parameters.<n>Third, we distill the model into symbolic surrogate models using block-wise regression, producing closed-form equations that eliminate trainable computation.
• Score: 3.571534406261392
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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