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GNNet@CoNEXT 2022 : Graph Neural Networking Workshop (co-located with ACM CoNEXT 2022)

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Link: https://bnn.upc.edu/workshops/gnnet2022/
 
When Dec 9, 2022 - Dec 9, 2022
Where Rome, Italy
Abstract Registration Due Sep 9, 2022
Submission Deadline Sep 16, 2022
Notification Due Oct 17, 2022
Final Version Due Oct 25, 2022
Categories    networking   graph neural networks   machine learning   communications
 

Call For Papers


CALL FOR PAPERS

1st Graph Neural Networking Workshop (GNNet)

Co-located with ACM CoNEXT 2022
Rome, Italy, December 9, 2022

https://bnn.upc.edu/workshops/gnnet2022


We are glad to announce the first edition of the “Graph Neural Networking
Workshop 2022”, which is organized as part of ACM CoNEXT 2022, to be held in
Rome, Italy.

All accepted papers will be included in the conference proceedings and be made
available in the ACM Digital Library.

IMPORTANT DATES
===============

Paper registration deadline: September 9, 2022
Paper submission deadline: September 16, 2022
Paper acceptance notifications: October 17, 2022
Camera ready due: October 25, 2022

MOTIVATION
==========

While AI/ML is today mainstream in domains such as computer vision and speech
recognition, traditional AI/ML approaches have produced below-par results in
many networking applications. Proposed AI/ML solutions in networking do not
properly generalize, can be unreliable and ineffective in real-network
deployments, and are in general unable to properly deal with the strong
dynamics and changes (i.e., concept drift) occurring in networking applications.

Graphs are emerging as an abstraction to represent complex data. Computer
Networks are fundamentally graphs, and many of their relevant characteristics
– such as topology and routing – are represented as graph-structured data.
Machine learning, especially deep representation learning, on graphs is an
emerging field with a wide array of applications. Within this field, Graph
Neural Networks (GNNs) have been recently proposed to model and learn over
graph-structured data. Due to their unique ability to generalize over graph
data, GNNs are a central tool to apply AI/ML techniques to networking
applications.

GOALS
=====

The goal of GNNet is to leverage graph data representations and modern GNN
technology to advance the application of AI/ML in networking. GNNet provides
the first dedicated venue to present and discuss the latest advancements on
GNNs and general AI/ML on graphs applied to networking problems. GNNet will
bring together leaders from academia and industry to showcase recent
methodological advances of GNNs and their application to networking problems,
covering a wide range of applications and practical challenges for large-scale
training and deployment.

We expect GNNet would serve as the meeting point for the growing community on
this fascinating domain, which has currently not a specific forum for sharing
and discussion.

The GNNet workshop seeks for contributions in the field of GNNs and graph-based
learning applied to data communication networking problems, including the
analysis of on-line and off-line massive datasets, network traffic traces,
topological data, cybersecurity, performance measurements, and more. GNNet also
encourages novel and out-of-the-box approaches and use cases related to the
application of GNNs in networking. The workshop will allow researchers and
practitioners to discuss the open issues related to the application of GNNs and
graph-based learning to networking problems and to share new ideas and
techniques for big data analysis and AI/ML in data communication networks.

TOPICS OF INTEREST
==================

We encourage both mature and positioning submissions describing systems,
platforms, algorithms and applications addressing all facets of the application
of GNNs and Machine learning on graphs to the analysis of data communication
networks. We are particularly interesting in disruptive and novel ideas that
permit to unleash the power of GNNs in the networking domain. The following is
a non-exhaustive list of topics:

- GNNs and graph-based learning in networking applications
- Representation Learning on networking-related graphs
- Application of GNNs to network and service management
- Application of GNNs to network security and anomaly detection
- Application of GNNs to detection of malware, botnets, intrusions, phishing,
and abuse detection
- Adversarial learning for GNN-driven networking applications
- GNNs for data generation and digital twining in networking
- Temporal and dynamic GNNs in networking
- Graph-based analytics for visualization of complex networking applications
- Libraries, benchmarks, and datasets for GNN-based networking applications
- Scalability of GNNs for networking applications
- Explainability, fairness, accountability, transparency, and privacy issues in
GNN-based networking
- Challenges, pitfalls, and negative results in applying GNNs to networking
applications

SPECIAL SESSION
===============

GNNet would include a dedicated special session where the top teams competing
at the third edition of the Graph Neural Networking Challenge
(https://bnn.upc.edu/challenge/gnnet2022/) would be invited to present the
winning solutions of the challenge, providing an excellent complement to the
main program.

SUBMISSION INSTRUCTIONS
=======================

Submissions must be original, unpublished work, and not under consideration at
another conference or journal. Submitted papers must be at most six (6) pages
long, including all figures, tables, references, and appendices in two-column
10pt ACM format. Papers must include authors names and affiliations for
single-blind peer reviewing by the PC. Authors of accepted papers are expected
to present their papers at the workshop.

All accepted papers will be included in the conference proceedings and be made
available in the ACM Digital Library.

WORKSHOP CHAIRS
================

Pere Barlet-Ros, BNN-UPC, Spain
Pedro Casas, AIT, Austria
Franco Scarselli, University of Siena, Italy
Xiangle Cheng, Huawei, China
Albert Cabellos, BNN-UPC, Spain

PRELIMINARY PC COMMITTEE
========================

Lilian Berton, University of Sao Paulo, Brazil
Albert Bifet, Télécom ParisTech & University of Waikato, New Zealand
Laurent Ciavaglia, Rakuten, Japan
Constantine Dovrolis, Georgia Tech, USA
Lluís Fàbrega, UdG, Spain
Jerome François, INRIA, France
Fabien Geyer, Technical University of Munich, Germany
Matthias Herlich, Salzburg Research, Austria
Zied Ben Houidi, Huawei Technologies, France
Wolfgang Kellerer, Technical University of Munich, Germany
Federico Larroca, Universidad de la República, Uruguay
Alina Lazar, Youngstown State University, USA
Gonzalo Mateos, University of Rochester, USA
Christoph Neumann, Broadpeak, France
Diego Perino, Telefonica Research, Spain
Alejandro Ribeiro, University of Pennsylvania, USA
Krzysztof Rusek, AGH University of Science and Technology, Poland
José Suárez-Varela, BNN-UPC, Spain
Stefano Traverso, Ermes Cyber Security, Italy

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