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Found 15 papers, 8 papers with code
Detecting Contextual Network Anomalies with Graph Neural Networks
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.
GraphCC: A Practical Graph Learning-based Approach to Congestion Control in Datacenters
The ECN configuration is thus a crucial aspect on the performance of CC protocols.
MAGNNETO: A Graph Neural Network-based Multi-Agent system for Traffic Engineering
In this paper, we present MAGNNETO, a distributed ML-based framework that leverages Multi-Agent Reinforcement Learning and Graph Neural Networks for distributed TE optimization.
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.
RouteNet-Erlang: A Graph Neural Network for Network Performance Evaluation
Network modeling is a fundamental tool in network research, design, and operation.
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).
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.
IGNNITION: Bridging the Gap Between Graph Neural Networks and Networking Systems
In this article, we present IGNNITION, a novel open-source framework that enables fast prototyping of GNNs for networking systems.
Is Machine Learning Ready for Traffic Engineering Optimization?
In our evaluation, we compare our MARL+GNN system with DEFO, a network optimizer based on Constraint Programming that represents the state of the art in TE.
BIG-bench Machine Learning
Multi-agent Reinforcement Learning
+1
Unveiling the potential of Graph Neural Networks for robust Intrusion Detection
To this end, we use a graph representation that keeps flow records and their relationships, and propose a novel Graph Neural Network (GNN) model tailored to process and learn from such graph-structured information.
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.
Applying Graph-based Deep Learning To Realistic Network Scenarios
Recent advances in Machine Learning (ML) have shown a great potential to build data-driven solutions for a plethora of network-related problems.
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
