[RACI-list] [1st CoNEXT GNNet Workshop] Graph Neural Networking Workshop, co-located with CoNEXT 2022, Rome, Italy, December 9, 2022 -- Register your paper by Friday!

[Apologies for cross-posting]

1st GNNet Workshop
Graph Neural Networking Workshop
Co-located with ACM CoNEXT 2022
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.



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.


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

Submissions’ site -- https://conext-gnnet2022.hotcrp.com/


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


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


  PROGRAM 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
  * Dario Rossi, Huawei Technologies, France
  * Krzysztof Rusek, AGH University of Science and Technology, Poland
  * José Suárez-Varela. BNN-UPC, Spain
  * Stefano Traverso, Ermes Cyber Security, Italy

Thanks,

Jordi Paillissé
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