Search Results for author: Shihan Xiao

Found 12 papers, 6 papers with code

Graph Neural Networks for Communication Networks: Context, Use Cases and Opportunities

1 code implementation29 Dec 2021 José Suárez-Varela, Paul Almasan, Miquel Ferriol-Galmés, Krzysztof Rusek, Fabien Geyer, Xiangle Cheng, Xiang Shi, Shihan Xiao, Franco Scarselli, Albert Cabellos-Aparicio, Pere Barlet-Ros

Graph neural networks (GNN) have shown outstanding applications in many fields where data is fundamentally represented as graphs (e. g., chemistry, biology, recommendation systems).

Management Recommendation Systems

RouteNet-Fermi: Network Modeling with Graph Neural Networks

2 code implementations22 Dec 2022 Miquel Ferriol-Galmés, Jordi Paillisse, José Suárez-Varela, Krzysztof Rusek, Shihan Xiao, Xiang Shi, Xiangle Cheng, Pere Barlet-Ros, Albert Cabellos-Aparicio

We have tested RouteNet-Fermi in networks of increasing size (up to 300 nodes), including samples with mixed traffic profiles -- e. g., with complex non-Markovian models -- and arbitrary routing and queue scheduling configurations.

Scheduling

Flow Neural Network and Flow-Structured Data Representation

no code implementations1 Jan 2021 Xiangle Cheng, Yuchen He, Feifei Long, Shihan Xiao, FengLin Li

Traffic flows are the most fundamental components in a communication networking system.

Physics Constrained Flow Neural Network for Short-Timescale Predictions in Data Communications Networks

no code implementations23 Dec 2021 Xiangle Cheng, James He, Shihan Xiao, Yingxue Zhang, Zhitang Chen, Pascal Poupart, FengLin Li

Machine learning is gaining growing momentum in various recent models for the dynamic analysis of information flows in data communications networks.

Self-Supervised Learning

MAGNNETO: A Graph Neural Network-based Multi-Agent system for Traffic Engineering

no code implementations31 Mar 2023 Guillermo Bernárdez, José Suárez-Varela, Albert López, Xiang Shi, Shihan Xiao, Xiangle Cheng, Pere Barlet-Ros, Albert Cabellos-Aparicio

In this paper, we present MAGNNETO, a distributed ML-based framework that leverages Multi-Agent Reinforcement Learning and Graph Neural Networks for distributed TE optimization.

Multi-agent Reinforcement Learning

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