Network modeling is a fundamental tool in network research, design, and operation.
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.
Nowadays innovation is one of the main determinants of economic development.
In the paper, we propose two models of Artificial Intelligence (AI) patents in European Union (EU) countries addressing spatial and temporal behaviour.
1 code implementation • 29 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).
Graph Neural Networks (GNN) have shown a strong potential to be integrated into commercial products for network control and management.
1 code implementation • 26 Jul 2021 • José Suárez-Varela, Miquel Ferriol-Galmés, Albert López, Paul Almasan, Guillermo Bernárdez, David Pujol-Perich, Krzysztof Rusek, Loïck Bonniot, Christoph Neumann, François Schnitzler, François Taïani, Martin Happ, Christian Maier, Jia Lei Du, Matthias Herlich, Peter Dorfinger, Nick Vincent Hainke, Stefan Venz, Johannes Wegener, Henrike Wissing, Bo Wu, Shihan Xiao, Pere Barlet-Ros, Albert Cabellos-Aparicio
During the last decade, Machine Learning (ML) has increasingly become a hot topic in the field of Computer Networks and is expected to be gradually adopted for a plethora of control, monitoring and management tasks in real-world deployments.
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.
Network modeling is a key enabler to achieve efficient network operation in future self-driving Software-Defined Networks.
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
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