The graph neural networking challenge

@article{SurezVarela2021TheGN,
  title={The graph neural networking challenge},
  author={Jos{\'e} Su{\'a}rez-Varela and Miquel Ferriol Galm{\'e}s and Albert Lopez and Paul Almasan and Guillermo Bern{\'a}rdez and David Pujol-Perich and Krzysztof Rusek and Lo{\"i}ck Bonniot and Christoph Neumann and François Schnitzler and François Ta{\"i}ani and Martin Happ and Christian Maier and Jia Lei Du and Matthias Herlich and Peter Dorfinger and Nick Vincent Hainke and Stefan Venz and John A. Wegener and Henrike Wissing and Bo Wu and Shihan Xiao and Pere Barlet-Ros and Albert Cabellos-Aparicio},
  journal={ACM SIGCOMM Computer Communication Review},
  year={2021},
  volume={51},
  pages={9 - 16}
}
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. This poses the need to count on new generations of students, researchers and practitioners with a solid background in ML applied to networks. During 2020, the International Telecommunication Union (ITU) has organized the "ITU AI/ML in 5G challenge", an open… Expand
2 Citations

Figures and Tables from this paper

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 andExpand
IGNNITION: fast prototyping of graph neural networks for communication networks
TLDR
IGNNITION comprises a set of tools and functionalities that eases and accelerates the whole implementation process, from the design of a GNN model, to its training, evaluation, debugging, and integration into larger network applications. Expand

References

SHOWING 1-10 OF 45 REFERENCES
Machine Learning for Networking: Workflow, Advances and Opportunities
TLDR
The basic workflow to explain how to apply machine learning technology in the networking domain is summarized and a selective survey of the latest representative advances with explanations of their design principles and benefits is provided. Expand
A comprehensive survey on machine learning for networking: evolution, applications and research opportunities
TLDR
This survey delineates the limitations, give insights, research challenges and future opportunities to advance ML in networking, and jointly presents the application of diverse ML techniques in various key areas of networking across different network technologies. Expand
Deep Reinforcement Learning meets Graph Neural Networks: exploring a routing optimization use case.
TLDR
This paper proposes to use Graph Neural Networks (GNN) in combination with DRL, and its novel DRL+GNN architecture is able to learn, operate and generalize over arbitrary network topologies. Expand
Learning and Generating Distributed Routing Protocols Using Graph-Based Deep Learning
TLDR
This paper proposes an approach for applying graph-based deep learning on distributed routing protocols via a novel neural network architecture named Graph-Query Neural Network and shows that specific properties of network protocols, such as resilience to packet loss, can be explicitly included in the learned protocol. Expand
Relational inductive biases, deep learning, and graph networks
TLDR
It is argued that combinatorial generalization must be a top priority for AI to achieve human-like abilities, and that structured representations and computations are key to realizing this objective. Expand
Unveiling the potential of Graph Neural Networks for network modeling and optimization in SDN
TLDR
A novel Graph Neural Network model able to understand the complex relationship between topology, routing and input traffic to produce accurate estimates of the per-source/destination pair mean delay and jitter is proposed. Expand
Graph Neural Networks: A Review of Methods and Applications
TLDR
A detailed review over existing graph neural network models is provided, systematically categorize the applications, and four open problems for future research are proposed. Expand
Deep Learning in Mobile and Wireless Networking: A Survey
TLDR
This paper bridges the gap between deep learning and mobile and wireless networking research, by presenting a comprehensive survey of the crossovers between the two areas, and provides an encyclopedic review of mobile and Wireless networking research based on deep learning, which is categorize by different domains. Expand
Experience-driven Networking: A Deep Reinforcement Learning based Approach
TLDR
A novel experience-driven approach that can learn to well control a communication network from its own experience rather than an accurate mathematical model, just as a human learns a new skill (such as driving, swimming, etc). Expand
The Graph Neural Network Model
TLDR
A new neural network model, called graph neural network (GNN) model, that extends existing neural network methods for processing the data represented in graph domains, and implements a function tau(G,n) isin IRm that maps a graph G and one of its nodes n into an m-dimensional Euclidean space. Expand
...
1
2
3
4
5
...