363 citations · 2.1k across the 64 of their papers we have counts for
66 papers
RouteNet-Gauss: Hardware-Enhanced Network Modeling with Machine Learning
Carlos Güemes-Palau, Miquel Ferriol-Galmés, Jordi Paillisse-Vilanova +3
Network simulation is pivotal in network modeling, assisting with tasks ranging from capacity planning to performance estimation. Traditional approaches such as Discrete Event Simu…
Flow Optimization at Inter-Datacenter Networks for Application Run-time Acceleration
Berta Serracanta, Alberto Rodriguez-Natal, Fabio Maino +1
In the present-day, distributed applications are commonly spread across multiple datacenters, reaching out to edge and fog computing locations. The transition away from single data…
Detecting Contextual Network Anomalies with Graph Neural Networks
Hamid Latif-Martínez, José Suárez-Varela, Albert Cabellos-Aparicio +1
Detecting anomalies on network traffic is a complex task due to the massive amount of traffic flows in today's networks, as well as the highly-dynamic nature of traffic over time.…
Atom: Neural Traffic Compression with Spatio-Temporal Graph Neural Networks
Paul Almasan, Krzysztof Rusek, Shihan Xiao +4
Storing network traffic data is key to efficient network management; however, it is becoming more challenging and costly due to the ever-increasing data transmission rates, traffic…
Building a Graph-based Deep Learning network model from captured traffic traces
Carlos Güemes-Palau, Miquel Ferriol Galmés, Albert Cabellos-Aparicio +1
Currently the state of the art network models are based or depend on Discrete Event Simulation (DES). While DES is highly accurate, it is also computationally costly and cumbersome…
Enhancing 5G Radio Planning with Graph Representations and Deep Learning
Paul Almasan, José Suárez-Varela, Andra Lutu +2
The roll out of new mobile network generations poses hard challenges due to various factors such as cost-benefit tradeoffs, existing infrastructure, and new technology aspects. In…