Search Results for author: Krzysztof Rusek

Found 11 papers, 5 papers with code

RiskNet: Neural Risk Assessment in Networks of Unreliable Resources

no code implementations28 Jan 2022 Krzysztof Rusek, Piotr Boryło, Piotr Jaglarz, Fabien Geyer, Albert Cabellos, Piotr Chołda

We propose a graph neural network (GNN)-based method to predict the distribution of penalties induced by outages in communication networks, where connections are protected by resources shared between working and backup paths.

Bayesian inference of spatial and temporal relations in AI patents for EU countries

no code implementations18 Jan 2022 Krzysztof Rusek, Agnieszka Kleszcz, Albert Cabellos-Aparicio

In the paper, we propose two models of Artificial Intelligence (AI) patents in European Union (EU) countries addressing spatial and temporal behaviour.

Bayesian Inference

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).

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.

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 reinforcement-learning

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.

Generalized Score Distribution

1 code implementation10 Sep 2019 Lucjan Janowski, Bogdan Ćmiel, Krzysztof Rusek, Jakub Nawała, Zhi Li

A class of discrete probability distributions contains distributions with limited support, i. e. possible argument values are limited to a set of numbers (typically consecutive).

Methodology Multimedia G.3

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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