Search Results for author: José Suárez-Varela

Found 10 papers, 5 papers with code

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

no code implementations29 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).

Recommendation Systems

Scaling Graph-based Deep Learning models to larger networks

no code implementations4 Oct 2021 Miquel Ferriol-Galmés, José Suárez-Varela, Krzysztof Rusek, Pere Barlet-Ros, Albert Cabellos-Aparicio

Graph Neural Networks (GNN) have shown a strong potential to be integrated into commercial products for network control and management.

Is Machine Learning Ready for Traffic Engineering Optimization?

1 code implementation3 Sep 2021 Guillermo Bernárdez, José Suárez-Varela, Albert López, Bo Wu, Shihan Xiao, Xiangle Cheng, Pere Barlet-Ros, Albert Cabellos-Aparicio

In our evaluation, we compare our MARL+GNN system with DEFO, a network optimizer based on Constraint Programming that represents the state of the art in TE.

Multi-agent Reinforcement Learning

Unveiling the potential of Graph Neural Networks for robust Intrusion Detection

1 code implementation30 Jul 2021 David Pujol-Perich, José Suárez-Varela, Albert Cabellos-Aparicio, Pere Barlet-Ros

To this end, we use a graph representation that keeps flow records and their relationships, and propose a novel Graph Neural Network (GNN) model tailored to process and learn from such graph-structured information.

Network Intrusion Detection

Applying Graph-based Deep Learning To Realistic Network Scenarios

no code implementations13 Oct 2020 Miquel Ferriol-Galmés, José Suárez-Varela, Pere Barlet-Ros, Albert Cabellos-Aparicio

Recent advances in Machine Learning (ML) have shown a great potential to build data-driven solutions for a plethora of network-related problems.

Deep Reinforcement Learning meets Graph Neural Networks: exploring a routing optimization use case

1 code implementation16 Oct 2019 Paul Almasan, José Suárez-Varela, Arnau Badia-Sampera, Krzysztof Rusek, Pere Barlet-Ros, Albert Cabellos-Aparicio

However, most of the state-of-the-art DRL-based networking techniques fail to generalize, this means that they can only operate over network topologies seen during training, but not over new topologies.

Decision Making

RouteNet: Leveraging Graph Neural Networks for network modeling and optimization in SDN

no code implementations3 Oct 2019 Krzysztof Rusek, José Suárez-Varela, Paul Almasan, Pere Barlet-Ros, Albert Cabellos-Aparicio

Network modeling is a key enabler to achieve efficient network operation in future self-driving Software-Defined Networks.

Unveiling the potential of Graph Neural Networks for network modeling and optimization in SDN

3 code implementations23 Jan 2019 Krzysztof Rusek, José Suárez-Varela, Albert Mestres, Pere Barlet-Ros, Albert Cabellos-Aparicio

In the paper we show that our model provides accurate estimates of delay and jitter (worst case $R^2=0. 86$) when testing against topologies, routing and traffic not seen during training.

Networking and Internet Architecture

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