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[2602.12798] Can Neural Networks Provide Latent Embeddings for Telemetry-Aware Greedy Routing?
Summary
The paper explores a novel algorithm, Placer, which utilizes Message Passing Networks to create latent embeddings for telemetry-aware greedy routing in computer networks, enhancing routing efficacy and explainability.
Why It Matters
As networks become increasingly complex, traditional routing methods struggle to adapt to dynamic traffic conditions. This research addresses the need for more efficient and interpretable routing solutions using machine learning, potentially improving network performance and reliability.
Key Takeaways
- Placer algorithm transforms network states into latent embeddings for routing.
- Enhances routing decisions' explainability compared to traditional black-box models.
- Facilitates efficient next-hop routing without solving all-pairs shortest paths.
Computer Science > Machine Learning arXiv:2602.12798 (cs) [Submitted on 13 Feb 2026] Title:Can Neural Networks Provide Latent Embeddings for Telemetry-Aware Greedy Routing? Authors:Andreas Boltres, Niklas Freymuth, Gerhard Neumann View a PDF of the paper titled Can Neural Networks Provide Latent Embeddings for Telemetry-Aware Greedy Routing?, by Andreas Boltres and 2 other authors View PDF HTML (experimental) Abstract:Telemetry-Aware routing promises to increase efficacy and responsiveness to traffic surges in computer networks. Recent research leverages Machine Learning to deal with the complex dependency between network state and routing, but sacrifices explainability of routing decisions due to the black-box nature of the proposed neural routing modules. We propose \emph{Placer}, a novel algorithm using Message Passing Networks to transform network states into latent node embeddings. These embeddings facilitate quick greedy next-hop routing without directly solving the all-pairs shortest paths problem, and let us visualize how certain network events shape routing decisions. Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Networking and Internet Architecture (cs.NI) Cite as: arXiv:2602.12798 [cs.LG] (or arXiv:2602.12798v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2602.12798 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Andreas Boltres [view email] [v1] Fri, 13 Feb 2026 10:31:09 UT...
