<html> <head> <meta http-equiv="content-type" content="text/html; charset=UTF-8"> </head> <body> [Apologies for cross-posting] 1st GNNet Workshop Graph Neural Networking Workshop Co-located with ACM CoNEXT 2022 December 9, 2022 <a href="https://bnn.upc.edu/workshops/gnnet2022/" class="moz-txt-link-freetext">https://bnn.upc.edu/workshops/gnnet2022/</a> 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 (<a href="https://bnn.upc.edu/challenge/gnnet2022/" class="moz-txt-link-freetext">https://bnn.upc.edu/challenge/gnnet2022/</a>) 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 -- <a href="https://conext-gnnet2022.hotcrp.com/" class="moz-txt-link-freetext"> https://conext-gnnet2022.hotcrp.com/</a> 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: <ul style="margin-top:0cm" type="disc"> <li class="MsoListParagraph" style="margin-left:0cm;mso-list:l2 level1 lfo1">GNNs and graph-based learning in networking applications</li> <li class="MsoListParagraph" style="margin-left:0cm;mso-list:l2 level1 lfo1">Representation Learning on networking-related graphs</li> <li class="MsoListParagraph" style="margin-left:0cm;mso-list:l2 level1 lfo1">Application of GNNs to network and service management</li> <li class="MsoListParagraph" style="margin-left:0cm;mso-list:l2 level1 lfo1">Application of GNNs to network security and anomaly detection</li> <li class="MsoListParagraph" style="margin-left:0cm;mso-list:l2 level1 lfo1">Application of GNNs to detection of malware, botnets, intrusions, phishing, and abuse detection</li> <li class="MsoListParagraph" style="margin-left:0cm;mso-list:l2 level1 lfo1">Adversarial learning for GNN-driven networking applications</li> <li class="MsoListParagraph" style="margin-left:0cm;mso-list:l2 level1 lfo1">GNNs for data generation and digital twining in networking</li> <li class="MsoListParagraph" style="margin-left:0cm;mso-list:l2 level1 lfo1">Temporal and dynamic GNNs in networking</li> <li class="MsoListParagraph" style="margin-left:0cm;mso-list:l2 level1 lfo1">Graph-based analytics for visualization of complex networking applications</li> <li class="MsoListParagraph" style="margin-left:0cm;mso-list:l2 level1 lfo1">Libraries, benchmarks, and datasets for GNN-based networking applications</li> <li class="MsoListParagraph" style="margin-left:0cm;mso-list:l2 level1 lfo1">Scalability of GNNs for networking applications</li> <li class="MsoListParagraph" style="margin-left:0cm;mso-list:l2 level1 lfo1">Explainability, fairness, accountability, transparency, and privacy issues in GNN-based networking</li> <li class="MsoListParagraph" style="margin-left:0cm;mso-list:l2 level1 lfo1">Challenges, pitfalls, and negative results in applying GNNs to networking applications</li> </ul> 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 ================ <ul style="margin-top:0cm" type="disc"> <li class="MsoListParagraph" style="margin-left:0cm;mso-list:l1 level1 lfo2">Pere Barlet-Ros, BNN-UPC, Spain</li> <li class="MsoListParagraph" style="margin-left:0cm;mso-list:l1 level1 lfo2">Pedro Casas, AIT, Austria</li> <li class="MsoListParagraph" style="margin-left:0cm;mso-list:l1 level1 lfo2">Franco Scarselli, University of Siena, Italy</li> <li class="MsoListParagraph" style="margin-left:0cm;mso-list:l1 level1 lfo2">Xiangle Cheng, Huawei, China</li> <li class="MsoListParagraph" style="margin-left:0cm;mso-list:l1 level1 lfo2">Albert Cabellos, BNN-UPC, Spain</li> </ul> PROGRAM COMMITTEE =================== <ul style="margin-top:0cm" type="disc"> <li class="MsoListParagraph" style="margin-left:0cm;mso-list:l0 level1 lfo3">Lilian Berton, University of Sao Paulo, Brazil</li> <li class="MsoListParagraph" style="margin-left:0cm;mso-list:l0 level1 lfo3">Albert Bifet, Télécom ParisTech & University of Waikato, New Zealand</li> <li class="MsoListParagraph" style="margin-left:0cm;mso-list:l0 level1 lfo3">Laurent Ciavaglia, Rakuten, Japan</li> <li class="MsoListParagraph" style="margin-left:0cm;mso-list:l0 level1 lfo3">Constantine Dovrolis, Georgia Tech, USA</li> <li class="MsoListParagraph" style="margin-left:0cm;mso-list:l0 level1 lfo3">Lluís Fàbrega, UdG, Spain</li> <li class="MsoListParagraph" style="margin-left:0cm;mso-list:l0 level1 lfo3">Jerome François, INRIA, France</li> <li class="MsoListParagraph" style="margin-left:0cm;mso-list:l0 level1 lfo3">Fabien Geyer, Technical University of Munich, Germany</li> <li class="MsoListParagraph" style="margin-left:0cm;mso-list:l0 level1 lfo3">Matthias Herlich, Salzburg Research, Austria</li> <li class="MsoListParagraph" style="margin-left:0cm;mso-list:l0 level1 lfo3">Zied Ben Houidi, Huawei Technologies, France</li> <li class="MsoListParagraph" style="margin-left:0cm;mso-list:l0 level1 lfo3">Wolfgang Kellerer, Technical University of Munich, Germany</li> <li class="MsoListParagraph" style="margin-left:0cm;mso-list:l0 level1 lfo3">Federico Larroca, Universidad de la República, Uruguay</li> <li class="MsoListParagraph" style="margin-left:0cm;mso-list:l0 level1 lfo3">Alina Lazar, Youngstown State University, USA</li> <li class="MsoListParagraph" style="margin-left:0cm;mso-list:l0 level1 lfo3">Gonzalo Mateos, University of Rochester, USA</li> <li class="MsoListParagraph" style="margin-left:0cm;mso-list:l0 level1 lfo3">Christoph Neumann, Broadpeak, France</li> <li class="MsoListParagraph" style="margin-left:0cm;mso-list:l0 level1 lfo3">Diego Perino, Telefonica Research, Spain</li> <li class="MsoListParagraph" style="margin-left:0cm;mso-list:l0 level1 lfo3">Alejandro Ribeiro, University of Pennsylvania, USA</li> <li class="MsoListParagraph" style="margin-left:0cm;mso-list:l0 level1 lfo3">Dario Rossi, Huawei Technologies, France</li> <li class="MsoListParagraph" style="margin-left:0cm;mso-list:l0 level1 lfo3">Krzysztof Rusek, AGH University of Science and Technology, Poland</li> <li class="MsoListParagraph" style="margin-left:0cm;mso-list:l0 level1 lfo3">José Suárez-Varela. BNN-UPC, Spain</li> <li class="MsoListParagraph" style="margin-left:0cm;mso-list:l0 level1 lfo3">Stefano Traverso, Ermes Cyber Security, Italy</li> </ul> Thanks, Jordi Paillissé </body> </html>