28 citations · 34 across the 2 of their papers we have counts for
6 papers · 1 filter
The Graph Neural Networking Challenge: A Worldwide Competition for Education in AI/ML for Networks
José Suárez-Varela, Miquel Ferriol-Galmés, Albert López +21
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 contro…
Applying Graph-based Deep Learning To Realistic Network Scenarios
Miquel Ferriol-Galmés, José Suárez-Varela, Pere Barlet-Ros +1
Recent advances in Machine Learning (ML) have shown a great potential to build data-driven solutions for a plethora of network-related problems. In this context, building fast and…
Deep Reinforcement Learning meets Graph Neural Networks: exploring a routing optimization use case
Paul Almasan, José Suárez-Varela, Krzysztof Rusek +2
Deep Reinforcement Learning (DRL) has shown a dramatic improvement in decision-making and automated control problems. Consequently, DRL represents a promising technique to efficien…
RouteNet: Leveraging Graph Neural Networks for network modeling and optimization in SDN
Krzysztof Rusek, José Suárez-Varela, Paul Almasan +2
Network modeling is a key enabler to achieve efficient network operation in future self-driving Software-Defined Networks. However, we still lack functional network models able to…
Unveiling the potential of Graph Neural Networks for network modeling and optimization in SDN
Krzysztof Rusek, José Suárez-Varela, Albert Mestres +2
Network modeling is a critical component for building self-driving Software-Defined Networks, particularly to find optimal routing schemes that meet the goals set by administrators…
Reinventing NetFlow for OpenFlow Software-Defined Networks
José Suárez-Varela, Pere Barlet-Ros
Obtaining flow-level measurements, similar to those provided by Netflow/IPFIX, with OpenFlow is challenging as it requires the installation of an entry per flow in the flow tables.…