Barcelona Neural Networking Center
BNN has been created with the main goals of carrying fundamental research in the field of Graph Neural Network applied to Computer Networks
Barcelona
Pinned Repositories
datanetAPI
ENERO
Code used in the paper "ENERO: Efficient real-time WAN routing optimization with Deep Reinforcement Learning". In this paper, the DRL agent is implemented with the PPO algorithm
GNN-NIDS
GNNetworkingChallenge
RouteNet baseline for the Graph Neural Networking Challenge (https://bnn.upc.edu/challenge/)
GNNPapersCommNets
GNNPapersPowerNets
ignnition
Framework for fast prototyping of Graph Neural Networks
NetworkModelingDatasets
This repository contains datasets for network modeling simulated with OMNet++
Papers
A list of our publications.
RouteNet-Fermi
Barcelona Neural Networking Center's Repositories
BNN-UPC/GNNetworkingChallenge
RouteNet baseline for the Graph Neural Networking Challenge (https://bnn.upc.edu/challenge/)
BNN-UPC/NetworkModelingDatasets
This repository contains datasets for network modeling simulated with OMNet++
BNN-UPC/Papers
A list of our publications.
BNN-UPC/ignnition
Framework for fast prototyping of Graph Neural Networks
BNN-UPC/GNN-NIDS
BNN-UPC/GNNPapersCommNets
BNN-UPC/RouteNet-Fermi
BNN-UPC/datanetAPI
BNN-UPC/GNNPapersPowerNets
BNN-UPC/ENERO
Code used in the paper "ENERO: Efficient real-time WAN routing optimization with Deep Reinforcement Learning". In this paper, the DRL agent is implemented with the PPO algorithm
BNN-UPC/RouteNet-Erlang
BNN-UPC/DRL-ES-OTN
BNN-UPC/MARL-GNN-TE
BNN-UPC/BNNetSimulator
BNNetSimulator is a packet-level network simulator to generate datasets for research and analysis.
BNN-UPC/MAGNNETO-TE
BNN-UPC/graphlaxy
BNN-UPC/TwinNet
BNN-UPC/Atom_Neural_Traffic_Compression
This repository contains de code and instructions to train the models and prepare the datasets for the experiments in the paper "Atom: Neural Traffic Compression with Spatio-Temporal Graph Neural Networks" accepted at the 2nd ACM CONEXT GNNet 2023 Workshop.
BNN-UPC/Wavelet_RouteNet_Fermi