{"ID":2825333,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.20885","arxiv_id":"2512.20885","title":"From GNNs to Symbolic Surrogates via Kolmogorov-Arnold Networks for Delay Prediction","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.","short_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 Kolm...","url_abs":"https://arxiv.org/abs/2512.20885","url_pdf":"https://arxiv.org/pdf/2512.20885v2","authors":"[\"Sami Marouani\",\"Kamal Singh\",\"Baptiste Jeudy\",\"Amaury Habrard\"]","published":"2025-12-24T02:05:46Z","proceeding":"cs.LG","tasks":"[\"cs.LG\",\"cs.NI\"]","methods":"[\"Graph Neural Network\"]","has_code":false}