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Neural collaborative filtering for network delay matrix completion

Sanaa GHANDI 1, 2 Alexandre Reiffers-Masson 1, 2 Sandrine Vaton 1, 2 Thierry Chonavel 3, 4 
1 Lab-STICC_MATHNET - Equipe Math & Net
Lab-STICC - Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance : UMR6285
3 Lab-STICC_MATRIX - Equipe Models and AlgoriThms for pRocessIng and eXtracting information
Lab-STICC - Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance : UMR6285
Abstract : In network monitoring, delays are of great use when it comes to QoS or content distributed services. However, it is often impossible to have access to all the delay measurements within a network. This can be due to network failures or to established measurement policies. For these reasons, delay matrix completion techniques are important for an optimal network monitoring service. In this paper, we formulate the completion problem as a neural collaborative filtering problem by testing two different architectures, generalized matrix factorization and multi-layer perceptron. We evaluate these methods on two different datasets: a synthetic one generated by an autonomous system simulator, and a real-world dataset from Ripe Atlas platform. Finally, a comparative study is conducted between these neural collaborative filtering methods and standard approaches.
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https://hal.archives-ouvertes.fr/hal-03775558
Contributor : Sanaa GHANDI Connect in order to contact the contributor
Submitted on : Monday, September 12, 2022 - 4:54:51 PM
Last modification on : Thursday, September 22, 2022 - 5:08:57 AM

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Distributed under a Creative Commons Attribution 4.0 International License

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  • HAL Id : hal-03775558, version 1

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Sanaa GHANDI, Alexandre Reiffers-Masson, Sandrine Vaton, Thierry Chonavel. Neural collaborative filtering for network delay matrix completion. 18th International Conference on Network and Service Management, Oct 2022, Thessalonique, Greece. ⟨hal-03775558⟩

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