RIGEL: Real-time Optical Anomaly Diagnosis with Stateful In-Network Inference based on Distributed On-switch GNNs | Paper Citation Record

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This paper cites https://www.barefootnetworks.

RIGEL: Real-time Optical Anomaly Diagnosis with Stateful In-Network Inference based on Distributed On-switch GNNs https://www.barefootnetworks

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RIGEL: Real-time Optical Anomaly Diagnosis with Stateful In-Network Inference based on Distributed On-switch GNNs Shale: A practical, scalable oblivious reconfigurable network

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This paper cites FlowLens: Enabling efficient flow classification for ML- based network security applications.

RIGEL: Real-time Optical Anomaly Diagnosis with Stateful In-Network Inference based on Distributed On-switch GNNs FlowLens: Enabling efficient flow classification for ML- based network security applications

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Observation ab317756-84dd-41c9-8ae2-7ccb1a697497 · outbound

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RIGEL: Real-time Optical Anomaly Diagnosis with Stateful In-Network Inference based on Distributed On-switch GNNs P4: Programming protocol-independent packet processors

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RIGEL: Real-time Optical Anomaly Diagnosis with Stateful In-Network Inference based on Distributed On-switch GNNs Forwarding metamorphosis: Fast programmable match-action processing in hardware for SDN.ACM SIGCOMM Computer Communication Re- view, 43(4):99–110, 2013

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Observation 5bda1fbd-177b-4c3f-900c-2be95182b0c1 · outbound

This paper cites pForest: In-Network Inference with Random Forests.

RIGEL: Real-time Optical Anomaly Diagnosis with Stateful In-Network Inference based on Distributed On-switch GNNs pForest: In-Network Inference with Random Forests

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This paper cites Dune: Distributed infer- ence in the user plane.

RIGEL: Real-time Optical Anomaly Diagnosis with Stateful In-Network Inference based on Distributed On-switch GNNs Dune: Distributed infer- ence in the user plane

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This paper cites Automating optical network fault man- agement with machine learning.IEEE Communications Magazine, 60(12):88–94, 2022.

RIGEL: Real-time Optical Anomaly Diagnosis with Stateful In-Network Inference based on Distributed On-switch GNNs Automating optical network fault man- agement with machine learning.IEEE Communications Magazine, 60(12):88–94, 2022

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This paper cites On cooperative fault man- agement in multi-domain optical networks using hybrid learning.IEEE Journal of Selected Topics in Quantum Electronics, 28(4):1–9, 2022.

RIGEL: Real-time Optical Anomaly Diagnosis with Stateful In-Network Inference based on Distributed On-switch GNNs On cooperative fault man- agement in multi-domain optical networks using hybrid learning.IEEE Journal of Selected Topics in Quantum Electronics, 28(4):1–9, 2022

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This paper cites Fernandez De Jauregui Ruiz, Amirhossein Ghazisaeidi, Thierry Zami, Sabine Louis, and Bruno Lavigne.

RIGEL: Real-time Optical Anomaly Diagnosis with Stateful In-Network Inference based on Distributed On-switch GNNs Fernandez De Jauregui Ruiz, Amirhossein Ghazisaeidi, Thierry Zami, Sabine Louis, and Bruno Lavigne

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RIGEL: Real-time Optical Anomaly Diagnosis with Stateful In-Network Inference based on Distributed On-switch GNNs Vector quantization.IEEE Assp Magazine, 1(2):4–29, 1984

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This paper cites In- ductive representation learning on large graphs.

RIGEL: Real-time Optical Anomaly Diagnosis with Stateful In-Network Inference based on Distributed On-switch GNNs In- ductive representation learning on large graphs

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This paper cites Semi-supervised classi- fication with graph convolutional networks.

RIGEL: Real-time Optical Anomaly Diagnosis with Stateful In-Network Inference based on Distributed On-switch GNNs Semi-supervised classi- fication with graph convolutional networks

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RIGEL: Real-time Optical Anomaly Diagnosis with Stateful In-Network Inference based on Distributed On-switch GNNs Distributed graph neural net- works in programmable data planes

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RIGEL: Real-time Optical Anomaly Diagnosis with Stateful In-Network Inference based on Distributed On-switch GNNs Optimizing telemetry forwarding for distributed failure recovery in packet-optical networks.Journal of Optical Commu- nications and Networking, 17(2):152–162, 2025

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RIGEL: Real-time Optical Anomaly Diagnosis with Stateful In-Network Inference based on Distributed On-switch GNNs Deeper insights into graph convolutional networks for semi- supervised learning

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This paper cites OpticGAI: Generative AI-aided deep reinforce- ment learning for optical networks optimization.

RIGEL: Real-time Optical Anomaly Diagnosis with Stateful In-Network Inference based on Distributed On-switch GNNs OpticGAI: Generative AI-aided deep reinforce- ment learning for optical networks optimization

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RIGEL: Real-time Optical Anomaly Diagnosis with Stateful In-Network Inference based on Distributed On-switch GNNs Scaling opti- cal network fault management with decentralized graph learning

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RIGEL: Real-time Optical Anomaly Diagnosis with Stateful In-Network Inference based on Distributed On-switch GNNs An al- gorithm for vector quantizer design.IEEE Transactions on communications, 28(1):84–95, 1980

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RIGEL: Real-time Optical Anomaly Diagnosis with Stateful In-Network Inference based on Distributed On-switch GNNs Lightwave fabrics: At- scale optical circuit switching for datacenter and ma- chine learning systems

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RIGEL: Real-time Optical Anomaly Diagnosis with Stateful In-Network Inference based on Distributed On-switch GNNs Jaqen: A high- performance switch-native approach for detecting and mitigating volumetric DDoS attacks with programmable switches

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RIGEL: Real-time Optical Anomaly Diagnosis with Stateful In-Network Inference based on Distributed On-switch GNNs A GAN based soft failure detection and identification frame- work for long-haul coherent optical communication sys- tems.Journal of Lightwave Technology, 41(8):2312– 2322, 2023

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RIGEL: Real-time Optical Anomaly Diagnosis with Stateful In-Network Inference based on Distributed On-switch GNNs Vector quantization in speech coding.Proceedings of the IEEE, 73(11):1551–1588, 1985

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RIGEL: Real-time Optical Anomaly Diagnosis with Stateful In-Network Inference based on Distributed On-switch GNNs Realiz- ing rotorNet: Toward practical microsecond scale optical networking

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RIGEL: Real-time Optical Anomaly Diagnosis with Stateful In-Network Inference based on Distributed On-switch GNNs Detecting ephemeral optical events with OpTel

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RIGEL: Real-time Optical Anomaly Diagnosis with Stateful In-Network Inference based on Distributed On-switch GNNs FlexWAN: Soft- ware hardware co-design for cost-effective and resilient optical backbones

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RIGEL: Real-time Optical Anomaly Diagnosis with Stateful In-Network Inference based on Distributed On-switch GNNs A tutorial on machine learning for failure man- agement in optical networks.Journal of Lightwave Technology, 37(16):4125–4139, 2019

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RIGEL: Real-time Optical Anomaly Diagnosis with Stateful In-Network Inference based on Distributed On-switch GNNs Failure management in optical networks with ML: a tutorial on applications, challenges, and pitfalls.Journal of Optical Communications and Networking, 17(8):C144–C155, 2025

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RIGEL: Real-time Optical Anomaly Diagnosis with Stateful In-Network Inference based on Distributed On-switch GNNs Forghieri

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RIGEL: Real-time Optical Anomaly Diagnosis with Stateful In-Network Inference based on Distributed On-switch GNNs Flex- gate: High-performance heterogeneous gateway in data centers

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RIGEL: Real-time Optical Anomaly Diagnosis with Stateful In-Network Inference based on Distributed On-switch GNNs Sales, A

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RIGEL: Real-time Optical Anomaly Diagnosis with Stateful In-Network Inference based on Distributed On-switch GNNs Can the network be the AI accelerator? InACM SIGCOMM Workshop on In-Network Computing (Net- Compute), pages 20–25, 2018

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RIGEL: Real-time Optical Anomaly Diagnosis with Stateful In-Network Inference based on Distributed On-switch GNNs Scaling distributed machine learning with in-network aggregation

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RIGEL: Real-time Optical Anomaly Diagnosis with Stateful In-Network Inference based on Distributed On-switch GNNs Learning from the optical spectrum: failure detection and identification.Journal of Lightwave Tech- nology, 37(2):433–440, 2019

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RIGEL: Real-time Optical Anomaly Diagnosis with Stateful In-Network Inference based on Distributed On-switch GNNs Re-architecting traffic analysis with neural network interface cards

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RIGEL: Real-time Optical Anomaly Diagnosis with Stateful In-Network Inference based on Distributed On-switch GNNs Running Neural Networks on the NIC

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RIGEL: Real-time Optical Anomaly Diagnosis with Stateful In-Network Inference based on Distributed On-switch GNNs Deep learning inference on com- modity network interface cards

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RIGEL: Real-time Optical Anomaly Diagnosis with Stateful In-Network Inference based on Distributed On-switch GNNs Digital residual spectrum-based generalized soft failure detection and identification in optical networks.IEEE Transactions on Communications, 71(1):324–338, 2022

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RIGEL: Real-time Optical Anomaly Diagnosis with Stateful In-Network Inference based on Distributed On-switch GNNs Taurus: A data plane architecture for per-packet ML

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RIGEL: Real-time Optical Anomaly Diagnosis with Stateful In-Network Inference based on Distributed On-switch GNNs Neural dis- crete representation learning.Advances in neural infor- mation processing systems (NeurIPS), 30, 2017

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RIGEL: Real-time Optical Anomaly Diagnosis with Stateful In-Network Inference based on Distributed On-switch GNNs BER degradation detection and failure identification in elastic optical networks

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RIGEL: Real-time Optical Anomaly Diagnosis with Stateful In-Network Inference based on Distributed On-switch GNNs Graph attention networks

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RIGEL: Real-time Optical Anomaly Diagnosis with Stateful In-Network Inference based on Distributed On-switch GNNs Fast texture synthesis using tree-structured vector quantization

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RIGEL: Real-time Optical Anomaly Diagnosis with Stateful In-Network Inference based on Distributed On-switch GNNs Programmable switches for in-networking classification

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RIGEL: Real-time Optical Anomaly Diagnosis with Stateful In-Network Inference based on Distributed On-switch GNNs Mousika: Enable general in-network intelligence in programmable switches by knowledge distillation

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RIGEL: Real-time Optical Anomaly Diagnosis with Stateful In-Network Inference based on Distributed On-switch GNNs Em- powering in-network classification in programmable switches by binary decision tree and knowledge dis- tillation.IEEE/ACM Transactions on Networking, 32(1):382–395, 2024

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RIGEL: Real-time Optical Anomaly Diagnosis with Stateful In-Network Inference based on Distributed On-switch GNNs Do switches dream of machine learning? Toward in-network classification

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RIGEL: Real-time Optical Anomaly Diagnosis with Stateful In-Network Inference based on Distributed On-switch GNNs Brain-on-Switch: To- wards advanced intelligent network data plane via NN- driven traffic analysis at line-speed

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RIGEL: Real-time Optical Anomaly Diagnosis with Stateful In-Network Inference based on Distributed On-switch GNNs P4pir: In-network analysis for smart iot gateways

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Observation 8049257e-425c-4dd5-af79-8491c3556650 · outbound

This paper cites Toward low-complexity neural networks for failure management in optical net- works.Journal of Optical Communications and Net- working, 17(7):555–563, 2025.

RIGEL: Real-time Optical Anomaly Diagnosis with Stateful In-Network Inference based on Distributed On-switch GNNs Toward low-complexity neural networks for failure management in optical net- works.Journal of Optical Communications and Net- working, 17(7):555–563, 2025

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-03T09:36:58.991293Z

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Observation 40cd6766-d317-4a8f-849a-c25e60a91120 · outbound

This paper cites Expertise-enhanced ma- chine learning for failure detection on field-deployed optical modules.Journal of Lightwave Technology, 43(1):137–154, 2025.

RIGEL: Real-time Optical Anomaly Diagnosis with Stateful In-Network Inference based on Distributed On-switch GNNs Expertise-enhanced ma- chine learning for failure detection on field-deployed optical modules.Journal of Lightwave Technology, 43(1):137–154, 2025

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-03T09:36:59.075729Z

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Observation 21c94aa0-0211-4c15-9efe-0b6b38dea6e9 · outbound

This paper cites A Machine Learning-Based Toolbox for P4 Programmable Data-Planes.IEEE Transactions on Network and Ser- vice Management, 21(4):4450–4465, 2024.

RIGEL: Real-time Optical Anomaly Diagnosis with Stateful In-Network Inference based on Distributed On-switch GNNs A Machine Learning-Based Toolbox for P4 Programmable Data-Planes.IEEE Transactions on Network and Ser- vice Management, 21(4):4450–4465, 2024

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-03T09:36:59.147795Z

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Observation b0485b7c-20f8-4ff0-9ba3-20df7e1c9784 · outbound

This paper cites MUTA: En- abling Multi-Task Neural Network Inference in Pro- grammable Data-Planes.

RIGEL: Real-time Optical Anomaly Diagnosis with Stateful In-Network Inference based on Distributed On-switch GNNs MUTA: En- abling Multi-Task Neural Network Inference in Pro- grammable Data-Planes

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-03T09:36:59.214106Z

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Observation 4663e5d6-f187-4a59-b22e-e1da4fc255cd · outbound

This paper cites Quark: Im- plementing convolutional neural networks entirely on programmable data plane.

RIGEL: Real-time Optical Anomaly Diagnosis with Stateful In-Network Inference based on Distributed On-switch GNNs Quark: Im- plementing convolutional neural networks entirely on programmable data plane

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-03T09:36:59.371881Z

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Observation 93643217-5691-449f-85e3-10fcab004c8c · outbound

This paper cites Pegasus: A universal framework for scalable deep learning inference on the dataplane.

RIGEL: Real-time Optical Anomaly Diagnosis with Stateful In-Network Inference based on Distributed On-switch GNNs Pegasus: A universal framework for scalable deep learning inference on the dataplane

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-03T09:36:59.490937Z

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Observation ced3b5f0-88c1-42c1-853e-9a6ea0959c66 · outbound

This paper cites Bui, Siim Kaupmees, Riyad Bensoussane, Antoine Bernabeu, Shay Vargaftik, Yaniv Ben-Itzhak, and Noa Zilber- man.

RIGEL: Real-time Optical Anomaly Diagnosis with Stateful In-Network Inference based on Distributed On-switch GNNs Bui, Siim Kaupmees, Riyad Bensoussane, Antoine Bernabeu, Shay Vargaftik, Yaniv Ben-Itzhak, and Noa Zilber- man

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-03T09:36:59.538971Z

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Observation 3bf214aa-d3de-4432-8f2d-fa010e580b52 · outbound

This paper cites Planter: Rapid Prototyping of In-Network Machine Learning Inference.

RIGEL: Real-time Optical Anomaly Diagnosis with Stateful In-Network Inference based on Distributed On-switch GNNs Planter: Rapid Prototyping of In-Network Machine Learning Inference

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-03T09:36:59.640506Z

Source-reported events for the cited work

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Observation e00c1a58-4258-4d93-87be-75a57e6f0ea5 · outbound

This paper cites When P4 meets run-to- completion architecture.

RIGEL: Real-time Optical Anomaly Diagnosis with Stateful In-Network Inference based on Distributed On-switch GNNs When P4 meets run-to- completion architecture

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-03T09:36:59.792518Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 37778d38-7ee7-40ff-baba-52952aa2a508 · outbound

This paper cites An efficient design of intelligent network data plane.

RIGEL: Real-time Optical Anomaly Diagnosis with Stateful In-Network Inference based on Distributed On-switch GNNs An efficient design of intelligent network data plane

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-03T09:36:59.976617Z

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Observation ee6aaa02-1742-4522-82e1-57ea392c8583 · outbound

This paper cites Dynamic service provisioning in elastic optical net- works with hybrid single-/multi-path routing.Journal of Lightwave Technology, 31(1):15–22, 2013.

RIGEL: Real-time Optical Anomaly Diagnosis with Stateful In-Network Inference based on Distributed On-switch GNNs Dynamic service provisioning in elastic optical net- works with hybrid single-/multi-path routing.Journal of Lightwave Technology, 31(1):15–22, 2013

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-03T09:37:00.160798Z

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