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arXiv Machine Learning By Shenjia Ding, David Flynn, Paul Harvey

Toward Composable Network Digital Twins: A Subgraph-Based Latency Prediction Study

Read the original on arXiv Machine Learning →

The paper proposes a composable network digital twin (NDT) that breaks down network topologies into reusable subgraph units, enabling efficient and accurate per-route latency prediction. By aggregating these unit twins, the approach maintains high in-distribution accuracy and remains stable when faced with out-of-distribution traffic or topology changes. Compared to monolithic NDTs, the composable method offers greater reusability without sacrificing predictive performance.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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