363 citations · 1.6k across the 44 of their papers we have counts for
8 papers · 1 filter
Power Flow Feasibility Assessment Using Variational Graph Autoencoders
Ferran Bohigas-Daranas, Hamid Latif-Martinez, Eduardo Prieto-Araujo +2
Data-driven methods, including graph neural networks, have been studied for accelerating power flow calculations in recent years, but very little attention has been paid to the sol…
GraphUniverse: Synthetic Graph Generation for Evaluating Inductive Generalization
Louis Van Langendonck, Guillermo Bernárdez, Nina Miolane +1
A fundamental challenge in graph learning is understanding how models generalize to new, unseen graphs. While synthetic benchmarks offer controlled settings for analysis, existing…
Ordered Topological Deep Learning: a Network Modeling Case Study
Guillermo Bernárdez, Miquel Ferriol-Galmés, Carlos Güemes-Palau +4
Computer networks are the foundation of modern digital infrastructure, facilitating global communication and data exchange. As demand for reliable high-bandwidth connectivity grows…
Towards a graph-based foundation model for network traffic analysis
Louis Van Langendonck, Ismael Castell-Uroz, Pere Barlet-Ros
Foundation models have shown great promise in various fields of study. A potential application of such models is in computer network traffic analysis, where these models can grasp…
PPT-GNN: A Practical Pre-Trained Spatio-Temporal Graph Neural Network for Network Security
Louis Van Langendonck, Ismael Castell-Uroz, Pere Barlet-Ros
Recent works have demonstrated the potential of Graph Neural Networks (GNN) for network intrusion detection. Despite their advantages, a significant gap persists between real-world…
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.…
Topological Graph Signal Compression
Guillermo Bernárdez, Lev Telyatnikov, Eduard Alarcón +3
Recently emerged Topological Deep Learning (TDL) methods aim to extend current Graph Neural Networks (GNN) by naturally processing higher-order interactions, going beyond the pairw…
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…