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BNNetSimulator image release
by Albert López 16 Jan '23

16 Jan '23

Dear members, After successfully completing one more edition of the Networking Challenge, we are proud to announce the release of BNNetSimulator, a new docker image of the NetSim simulator released during the current challenge but without its restrictions. You can find all required information in the following link: [https://github.com/BNN-UPC/BNNetSimulator] Best regards, Albert López on behalf of the GNNet Challenge Organizing Committee

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Call for participation: Graph Neural Networking Challenge Award Ceremony
by JOSE RAFAEL SUAREZ-VARELA MACIA 08 Dec '22

08 Dec '22

[Apologies if you receive multiple copies of this email] Dear colleagues, As we announced, the Award Ceremony of the Graph Neural Networking Challenge will be held on December 9, at 1:30pm-3:00pm CET. It will be organized in a hybrid format, as a special session within the 1st GNNet Workshop (co-located with ACM CoNEXT, in Rome, Italy). All of you are invited to join us online, please find the Zoom details below. Zoom link: https://us02web.zoom.us/j/85026049416 Meeting ID: 850 2604 9416 Agenda (1h 30 mins): • 1:30pm-2:00pm CET: Introduction and awards announcement (by José Suárez-Varela) • 2:00pm-2:45pm CET: Presentations of the top-3 teams (10 mins + 3-5 mins for Q&A each) - Winning team: Snowyowl team Title: Designing GNN Training Data with Limited Samples and Small Network Sizes Authors: Brigitte Jaumard, Junior Momo Ziazet, Charles Boudreau, Oscar Delgado (Concordia University) - 2nd team: Ghost Ducks Title: GNN Networking Challenge 2022 – Oracle based sampling Authors: Eli Sason, Eli Kravchik, Alexei Gaissinski, Yackov Lubarsky (Toga Networks, a Huawei company) - 3rd team: Net Title: Beta Distribution based Leave-one-out Sample Ranking Authors: Max Helm, Benedikt Jaeger (Technical University of Munich) • 2:45pm-3:00pm CET: Closing We look forward to seeing you there. Best regards, José Suárez-Varela ________________________________ Este mensaje y sus adjuntos se dirigen exclusivamente a su destinatario, puede contener información privilegiada o confidencial y es para uso exclusivo de la persona o entidad de destino. Si no es usted. el destinatario indicado, queda notificado de que la lectura, utilización, divulgación y/o copia sin autorización puede estar prohibida en virtud de la legislación vigente. Si ha recibido este mensaje por error, le rogamos que nos lo comunique inmediatamente por esta misma vía y proceda a su destrucción. The information contained in this transmission is confidential and privileged information intended only for the use of the individual or entity named above. If the reader of this message is not the intended recipient, you are hereby notified that any dissemination, distribution or copying of this communication is strictly prohibited. If you have received this transmission in error, do not read it. Please immediately reply to the sender that you have received this communication in error and then delete it. Esta mensagem e seus anexos se dirigem exclusivamente ao seu destinatário, pode conter informação privilegiada ou confidencial e é para uso exclusivo da pessoa ou entidade de destino. Se não é vossa senhoria o destinatário indicado, fica notificado de que a leitura, utilização, divulgação e/ou cópia sem autorização pode estar proibida em virtude da legislação vigente. Se recebeu esta mensagem por erro, rogamos-lhe que nos o comunique imediatamente por esta mesma via e proceda a sua destruição

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Invitation to the Graph Neural Networking Challenge Award Ceremony
by José Suárez-Varela 24 Nov '22

24 Nov '22

Dear participants, As we announced, the Award Ceremony of the Graph Neural Networking Challenge will be held on December 9, at 1:30pm-3:00pm CET. It will be organized in a hybrid format, as a special session within the 1st GNNet Workshop (co-located with ACM CoNEXT, in Rome, Italy). Please, find enclosed a calendar invite with a Zoom link to the event. *Agenda (1h 30 mins):* • 1:30pm-2:00pm CET:* Introduction and awards announcement* • 2:00pm-2:45pm CET: *Presentations of top-3 teams* (10 mins + 3-5 mins for Q&A each) - *Winning team: Snowyowl team* *Title*: Designing GNN Training Data with Limited Samples and Small Network Sizes *Authors*: Brigitte Jaumard, Junior Momo Ziazet, Charles Boudreau, Oscar Delgado (Concordia University) - *2nd team: Ghost Ducks* *Title*: GNN Networking Challenge 2022 – Oracle based sampling *Authors*: Eli Sason, Eli Kravchik, Alexei Gaissinski, Yackov Lubarsky (Toga Networks, a Huawei company) - *3rd team: Net* *Title*: Beta Distribution based Leave-one-out Sample Ranking *Authors*: Max Helm, Benedikt Jaeger (Technical University of Munich) • 2:45pm-3:00pm CET: *Closing* We look forward to seeing you all there. Best regards, José Suárez-Varela

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GNNet workshop co-located with ACM CoNEXT 2022
by Pere Barlet 16 Sep '22

16 Sep '22

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

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Round table for the Graph Neural Networking Challenge 2022 [Thu. Aug 4, at 14:00 – 15:00 CEST]
by José Suárez-Varela 02 Aug '22

02 Aug '22

Dear Colleagues, We have scheduled a round-table discussion for the Graph Neural Networking Challenge 2022. I hope it can be helpful for people interested in participating in the challenge, even if you have not registered yet. I will be happy to answer questions, receive feedback or give some tips. Please, save the date! *Date*: August 4, 2022 (Thur.) *Time*: 14:00 - 15:00 CEST Please register in advance for this meeting: *Registration*: https://itu.zoom.us/meeting/register/tJ0qfuGvpjosE9RdrWxt0rC0r8h1PTJAjn9F *Meeting ID*: 997 9878 1690 Looking forward to your participation. Best regards, José Suárez-Varela ------------------------ Postdoctoral Researcher Barcelona Neural Networking center (BNN-UPC) Universitat Politècnica de Catalunya

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Round table for the Graph Neural Networking Challenge 2022 [Thu. Aug 4, at 14:00 – 15:00 CEST]
by José Suárez-Varela 02 Aug '22

02 Aug '22

Dear Colleagues, We have scheduled a round-table discussion for the Graph Neural Networking Challenge 2022. I hope it can be helpful for people interested in participating in the challenge, even if you have not registered yet. I will be happy to answer questions, receive feedback or give some tips. Please, save the date! *Date*: August 4, 2022 (Thur.) *Time*: 14:00 - 15:00 CEST Please register in advance for this meeting: *Registration*: https://itu.zoom.us/meeting/register/tJ0qfuGvpjosE9RdrWxt0rC0r8h1PTJAjn9F *Meeting ID*: 997 9878 1690 Looking forward to your participation. Best regards, José Suárez-Varela ------------------------ Postdoctoral Researcher Barcelona Neural Networking center (BNN-UPC) Universitat Politècnica de Catalunya

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GNNet challenge 2022 – Improving Network Digital Twins through Data-centric AI
by José Suárez-Varela 20 Jul '22

20 Jul '22

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). 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

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Graph Neural Networking challenge 2022 - Kickoff webinar
by José Suárez-Varela 26 May '22

26 May '22

Dear Colleagues, We are happy to announce the third edition of the Graph Neural Networking challenge, titled: “Improving Network Digital Twins through Data-centric AI”. Tomorrow I will present a webinar about the challenge on the ITU AI for Good platform. The talk is open to all interested parties. I would like to encourage you to join this session, especially if you are planning to participate in the challenge this year: - Date: 27 May, 2022 (Fri.) - Time: 14:00 – 15:30 CEST - Presentation: Improving Network Digital Twins through Data-centric AI - Speakers: José Suárez-Varela; Postdoctoral Researcher, Barcelona Neural Networking center (BNN-UPC) - Registration URL: https://neuralnetwork.aiforgood.itu.int/event/ai-for-good/register?register… - Link: https://aiforgood.itu.int/event/improving-network-digital-twins-through-dat… *Graph Neural Networking challenge 2022* Website: https://bnn.upc.edu/challenge/gnnet2022/ (*the registration will open tomorrow*) Best regards, José Suárez-Varela Postdoctoral Researcher Barcelona Neural Networking center Universitat politècnica de Catalunya

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Award Ceremony of the Graph Neural Networking challenge 2021
by Jose Suárez-Varela 11 Nov '21

11 Nov '21

[Apologies if you received multiple copies of this email] Dear all, As you participated in past editions of the Graph Neural Networking challenge, we think this may be of your interest. We are delighted to invite you to the *award ceremony of the Graph Neural Networking challenge 2021*, which will be held online on *Friday, November 12 between 14.00–15.30 CET*. Please save the date and feel free to forward this invitation to any colleagues that may be interested. In this event, top-5 teams will present their solutions, and all of you will have the opportunity to ask questions and learn more about these solutions. This will be the agenda (1h 30 mins): 0:00-0:20: Introduction and award announcements (BNN team) 0:20-1:20: Presentations of the top-5 solutions 1:20-1:30: Closing Please, find the Zoom details below: https://us02web.zoom.us/j/81883789056?pwd=cnF4TnJhTlZ2WlVXRTRnS2FZQXBzZz09 Meeting ID: 818 8378 9056 Passcode: 581662 Phone one-tap: +12532158782,,81883789056#,,,,*581662# Estados Unidos (Tacoma) +13017158592,,81883789056#,,,,*581662# Estados Unidos (Washington DC) Looking forward to seeing you there. Best regards, José Suárez-Varela Postdoctoral researcher Barcelona Neural Networking center Universitat Politècnica de Catalunya

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Challenges-list