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<p class="MsoNormal"><span lang="EN-US">2<sup>nd</sup> GNNet Workshop<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US">Graph Neural Networking Workshop<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US">Co-located with ACM CoNEXT 2023 @Paris, FRANCE<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US">December 5-8, 2023<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US"><a href="https://bnn.upc.edu/workshops/gnnet2023/">https://bnn.upc.edu/workshops/gnnet2023/</a><o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US"><o:p> </o:p></span></p>
<p class="MsoNormal"><span lang="EN-US">We are glad to announce the second edition of the “Graph Neural Networking Workshop 2023”, which is organized as part of ACM CoNEXT 2023, to be held in Paris, France.<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US">All accepted papers will be included in the conference proceedings and be made available in the ACM Digital Library.<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US"><o:p> </o:p></span></p>
<p class="MsoNormal"><span lang="EN-US">IMPORTANT DATES<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US">================<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US">Paper submission deadline: September 8, 2023 (AoE)<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US">Paper acceptance notifications: October 18, 2023<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US">Camera ready due: October 25, 2023<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US"><o:p> </o:p></span></p>
<p class="MsoNormal"><span lang="EN-US">Submissions’ site </span><span lang="EN-US" style="font-family:Wingdings">à</span><span lang="EN-US">
<a href="https://conext-gnnet2023.hotcrp.com/">https://conext-gnnet2023.hotcrp.com/</a><o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US"><o:p> </o:p></span></p>
<p class="MsoNormal"><span lang="EN-US"><o:p> </o:p></span></p>
<p class="MsoNormal"><span lang="EN-US">SPECIAL SESSION<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US">==============<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US">GNNet would include a dedicated special session where the top teams competing at the fourth edition of the Graph Neural Networking Challenge (<a href="https://bnn.upc.edu/challenge/gnnet2023/">https://bnn.upc.edu/challenge/gnnet2023/</a>)
would be invited to present the winning solutions of the challenge, providing an excellent complement to the main program.<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US"><o:p> </o:p></span></p>
<p class="MsoNormal"><span lang="EN-US"><o:p> </o:p></span></p>
<p class="MsoNormal"><span lang="EN-US">MOTIVATION<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US">===========<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US">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.<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US"><o:p> </o:p></span></p>
<p class="MsoNormal"><span lang="EN-US">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.<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US"><o:p> </o:p></span></p>
<p class="MsoNormal"><span lang="EN-US"><o:p> </o:p></span></p>
<p class="MsoNormal"><span lang="EN-US">GOALS<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US">======<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US">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.<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US"><o:p> </o:p></span></p>
<p class="MsoNormal"><span lang="EN-US">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.<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US"><o:p> </o:p></span></p>
<p class="MsoNormal"><span lang="EN-US"><o:p> </o:p></span></p>
<p class="MsoNormal"><span lang="EN-US">TOPICS OF INTEREST<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US">=================<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US">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:<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US"><o:p> </o:p></span></p>
<p class="MsoNormal"><span lang="EN-US">• GNNs and graph-based learning in networking applications<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US">• Representation Learning on networking-related graphs<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US">• Application of GNNs to network and service management<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US">• Application of GNNs to network security and anomaly detection<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US">• Application of GNNs to detection of malware, botnets, intrusions, phishing, and abuse detection<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US">• Adversarial learning for GNN-driven networking applications<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US">• GNNs for data generation and digital twining in networking<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US">• Temporal and dynamic GNNs in networking<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US">• Graph-based analytics for visualization of complex networking applications<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US">• Libraries, benchmarks, and datasets for GNN-based networking applications<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US">• Scalability of GNNs for networking applications<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US">• Explainability, fairness, accountability, transparency, and privacy issues in GNN-based networking<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US">• Challenges, pitfalls, and negative results in applying GNNs to networking applications<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US"><o:p> </o:p></span></p>
<p class="MsoNormal"><span lang="EN-US"><o:p> </o:p></span></p>
<p class="MsoNormal"><span lang="EN-US">SUBMISSION INSTRUCTIONS<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US">=======================<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US">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.<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US"><o:p> </o:p></span></p>
<p class="MsoNormal"><span lang="EN-US">All accepted papers will be included in the conference proceedings and be made available in the ACM Digital Library.<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US"><o:p> </o:p></span></p>
<p class="MsoNormal"><span lang="EN-US"><o:p> </o:p></span></p>
<p class="MsoNormal"><span lang="EN-US">WORKSHOP CHAIRS<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US">================<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US">• Pere Barlet-Ros, BNN-UPC, Spain<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="ES">• Pedro Casas, AIT, Austria<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="ES">• Franco Scarselli, University of Siena, Italy<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="ES">• José Suárez-Varela, Telefónica Research, Spain<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US">• Albert Cabellos, BNN-UPC, Spain<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US"><o:p> </o:p></span></p>
<p class="MsoNormal"><span lang="EN-US">Best,<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US"><o:p> </o:p></span></p>
<p class="MsoNormal"><span lang="EN-US">Pedro </span><span lang="ES" style="font-family:"Segoe UI Emoji",sans-serif">😊</span><span lang="EN-US"><o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US"><o:p> </o:p></span></p>
<p class="MsoNormal"><b><span lang="EN-US" style="font-size:10.0pt;font-family:"Arial",sans-serif;color:black;mso-fareast-language:DE-AT">PEDRO CASAS<br>
</span></b><span lang="EN-US" style="font-family:"Arial",sans-serif;color:#7C8388;mso-fareast-language:DE-AT">Senior Scientist<br>
Data Science & Artificial Intelligence<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US" style="font-family:"Arial",sans-serif;color:#7C8388;mso-fareast-language:DE-AT">Center for Digital Safety & Security<br>
<br>
<b>AIT Austrian Institute of Technology GmbH<o:p></o:p></b></span></p>
<p class="MsoNormal"><span lang="EN-US" style="font-family:"Arial",sans-serif;color:#7C8388;mso-fareast-language:DE-AT">Giefinggasse 4 | 1210 Vienna | Austria<br>
T +43 50550-4104 | M +43 664 88256097 | F +43 50550-2813<br>
</span><span lang="EN-US" style="mso-fareast-language:DE-AT"><a href="mailto:pedro.casas@ait.ac.at"><span style="font-family:"Arial",sans-serif;color:blue">pedro.casas@ait.ac.at</span></a></span><span lang="EN-US" style="font-family:"Arial",sans-serif;color:#7C8388;mso-fareast-language:DE-AT">
| </span><span lang="EN-US" style="mso-fareast-language:DE-AT"><a href="http://www.ait.ac.at/"><span style="font-family:"Arial",sans-serif;color:blue">www.ait.ac.at</span></a></span><span lang="EN-US" style="font-family:"Arial",sans-serif;color:#7C8388;mso-fareast-language:DE-AT"><br>
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</span><span lang="EN-US" style="font-size:9.0pt;font-family:"Arial",sans-serif;color:#BEBEBE;mso-fareast-language:DE-AT">FN: 115980 i HG Wien | UID: ATU14703506<br>
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