Pere.

(on behalf of all workshop chairs and challenge organizers)

-------- Forwarded Message --------

Subject:	GNNet workshop co-located with ACM CoNEXT 2022
Date:	Mon, 25 Jul 2022 12:18:42 +0200
From:	Pere Barlet <pere.barlet@upc.edu>
To:	challenges-list@mail.bnn.upc.edu

Dear colleagues,

We are very excited to announce that this year we are organizing the first workshop on GNN applied to networks (GNNet 2022) co-located with ACM CoNEXT 2022 (Core Rank A): https://bnn.upc.edu/workshops/gnnet2022/

We encourage all challenge participants (either from this edition or past editions) to submit your challenge solution, or any other GNN-related research, to the GNNet workshop. All accepted papers will be included in the conference proceedings and be made available in the ACM Digital Library.

We plan to organize a specific session for challenge participants in the workshop.

You can find below the general CFP. Please note deadlines are very tight (we have to strictly adhere to CoNEXT timings) so, in order to be considered for publication, your paper should be submitted on September 16 (before the current edition of the challege ends). Also note that your solution does not necessarily be in the top of the ranking for the paper to be accepted for publication.

We hope this workshop can serve to build a small community among those of us interested in what GNN can bring to networking!

Do not hesitate to contact us if you have any questions or doubts!

Pere.

(on behalf of all workshop chairs and challenge organizers) 

--

                                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
    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