Challenges-list July 2022

challenges-list@bnn.upc.edu

2 participants
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GNNet workshop co-located with ACM CoNEXT 2022

by Pere Barlet

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 … [View More]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 [View Less]

2 years, 5 months

GNNet challenge 2022 – Improving Network Digital Twins through Data-centric AI

by José Suárez-Varela

Dear Colleagues, [As you registered in previous editions of the Graph Neural Networking challenge, we believe this information may be of your interest] We are glad to announce the *3rd edition of the Graph Neural Networking challenge*, organized as part of the "ITU AI/ML in 5G Challenge". *Title:* Improving Network Digital Twins through Data-centric AI *Website*: https://bnn.upc.edu/challenge/gnnet2022 *Registration is now open and free of charge for all participants *(use the link below).… [View More] Registration form: https://bnn.upc.edu/challenge/gnnet2022/registration Please contact us at the following email if you have any questions or comments: gnnetchallenge(a)bnn.upc.edu [INCENTIVES AND AWARDS] ======================= This year we will organize the *1st GNNet workshop*, *co-located with ACM CoNEXT* (December 2022). Top teams will be invited to present their solutions there. Please, find more details about this workshop below: https://bnn.upc.edu/workshops/gnnet2022 The winning team of the Graph Neural Networking challenge will receive a* cash prize of 1,000 CHF,* if the Judges Panel from the ITU AI/ML in 5G Challenge determines that the solution satisfies the judging criteria. Also, *top-3 teams* *will advance to the Grand Challenge Finale* of the "ITU AI/ML in 5G Challenge <https://aiforgood.itu.int/ai-ml-in-5g-challenge/>". Winners of the finale will receive the following prizes: · 1st prize: 3,000 CHF · Runner-up: 2,000 CHF [OVERVIEW] ========== In recent years, the networking community has produced robust Graph Neural Networks (GNN) that can accurately mimic complex network environments. Modern GNN architectures enable building lightweight and accurate Network Digital Twins that can operate in real time. However, the quality of ML-based models depends on two main components: the model architecture, and the training dataset. In this context, very little research has been done on the impact of training data on the performance of network models. The 3rd edition of the Graph Neural Networking challenge focuses on a fundamental problem of current ML-based solutions applied to networking: how to generate a good dataset. We invert the format of traditional ML competitions, which follow a model-centric approach. Instead, we propose to explore a data-centric approach for building accurate Network Digital Twins. [PROBLEM STATEMENT] =================== Participants will be given a state-of-the-art GNN model for network performance evaluation (RouteNet-Fermi), and a packet-level network simulator to generate datasets. They will be tasked with producing a training dataset that results in better performance for the target GNN model. [TIMELINE] ================ * Open registration: until Sep 30th 2022 * Evaluation phase: Oct 1st-Oct 15th 2022 * Final ranking and official announcement of top-3 teams: Nov 2022 * Award ceremony and presentations: Dec 2022 Best regards, José Suárez-Varela ------------------------ Postdoctoral Researcher Barcelona Neural Networking center (BNN-UPC) Universitat Politècnica de Catalunya [View Less]

2 years, 7 months

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Challenges-list July 2022