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Found 12 papers, 7 papers with code
Deep Reinforcement Learning meets Graph Neural Networks: exploring a routing optimization use case
GNNs are Deep Learning models inherently designed to generalize over graphs of different sizes and structures.
RouteNet: Leveraging Graph Neural Networks for network modeling and optimization in SDN
Network modeling is a key enabler to achieve efficient network operation in future self-driving Software-Defined Networks.
Unveiling the potential of Graph Neural Networks for network modeling and optimization in SDN
In the paper we show that our model provides accurate estimates of delay and jitter (worst case $R^2=0. 86$) when testing against topologies, routing and traffic not seen during training.
Networking and Internet Architecture
Graph Neural Networks for Communication Networks: Context, Use Cases and Opportunities
Graph neural networks (GNN) have shown outstanding applications in many fields where data is fundamentally represented as graphs (e. g., chemistry, biology, recommendation systems).
RouteNet-Fermi: Network Modeling with Graph Neural Networks
We have tested RouteNet-Fermi in networks of increasing size (up to 300 nodes), including samples with mixed traffic profiles -- e. g., with complex non-Markovian models -- and arbitrary routing and queue scheduling configurations.
Generalized Score Distribution
A class of discrete probability distributions contains distributions with limited support, i. e. possible argument values are limited to a set of numbers (typically consecutive).
Methodology Multimedia G.3
The Graph Neural Networking Challenge: A Worldwide Competition for Education in AI/ML for Networks
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 control, monitoring and management tasks in real-world deployments.
Scaling Graph-based Deep Learning models to larger networks
Graph Neural Networks (GNN) have shown a strong potential to be integrated into commercial products for network control and management.
Bayesian inference of spatial and temporal relations in AI patents for EU countries
In the paper, we propose two models of Artificial Intelligence (AI) patents in European Union (EU) countries addressing spatial and temporal behaviour.
Has EU accession boosted patents performance in the EU-13? -- A critical evaluation using causal impact analysis with Bayesian structural time-series models
Nowadays innovation is one of the main determinants of economic development.
RiskNet: Neural Risk Assessment in Networks of Unreliable Resources
We propose a graph neural network (GNN)-based method to predict the distribution of penalties induced by outages in communication networks, where connections are protected by resources shared between working and backup paths.
RouteNet-Erlang: A Graph Neural Network for Network Performance Evaluation
Network modeling is a fundamental tool in network research, design, and operation.
