activity
20212024
most citedIGNNITION: Bridging the Gap Between Graph Neural Networks and Networking Systems

23 citations · 60 across the 7 of their papers we have counts for

collaborators

7 papers

cs.NI2024★ 11 cited

Through the Telco Lens: A Countrywide Empirical Study of Cellular Handovers

Michail Kalntis, José Suárez-Varela, Jesús Omaña Iglesias +4

Cellular networks rely on handovers (HOs) as a fundamental element to enable seamless connectivity for mobile users. A comprehensive analysis of HOs can be achieved through data fr…

cs.LG2023★ 9 cited

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.…

cs.NI2021★ 5 cited

Scaling Graph-based Deep Learning models to larger networks

Miquel Ferriol-Galmés, José Suárez-Varela, Krzysztof Rusek +2

Graph Neural Networks (GNN) have shown a strong potential to be integrated into commercial products for network control and management. Early works using GNN have demonstrated an u…

cs.NI2021

Is Machine Learning Ready for Traffic Engineering Optimization?

Guillermo Bernárdez, José Suárez-Varela, Albert López +5

Traffic Engineering (TE) is a basic building block of the Internet. In this paper, we analyze whether modern Machine Learning (ML) methods are ready to be used for TE optimization.…

cs.LG2021★ 23 cited

IGNNITION: Bridging the Gap Between Graph Neural Networks and Networking Systems

David Pujol-Perich, José Suárez-Varela, Miquel Ferriol +4

Recent years have seen the vast potential of Graph Neural Networks (GNN) in many fields where data is structured as graphs (e.g., chemistry, recommender systems). In particular, GN…

cs.CR2021★ 1 cited

Unveiling the potential of Graph Neural Networks for robust Intrusion Detection

David Pujol-Perich, José Suárez-Varela, Albert Cabellos-Aparicio +1

The last few years have seen an increasing wave of attacks with serious economic and privacy damages, which evinces the need for accurate Network Intrusion Detection Systems (NIDS)…

cs.NI2021★ 11 cited

Towards Real-Time Routing Optimization with Deep Reinforcement Learning: Open Challenges

Paul Almasan, José Suárez-Varela, Bo Wu +3

The digital transformation is pushing the existing network technologies towards new horizons, enabling new applications (e.g., vehicular networks). As a result, the networking comm…