1st GNNet Workshop


Graph Neural Networking Workshop
Co-located with ACM CoNEXT 2022
December 9 - 2022​

We are glad to announce the “1st Graph Neural Networking Workshop 2022”, which is organized as part of “ACM CoNEXT 2022” to be held in Rome. All accepted papers will be included in the conference proceedings and be made available in the ACM Digital Library.

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.

Workshop goal

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 learing 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.

Special session

GNNet would also 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.

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 learing 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

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.

Please submit your papers at https://conext-gnnet2022.hotcrp.com/

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
  • Workshop date: December 9, 2022

Please note that these dates are tentative and may still change slightly in the next few weeks. Please stay tuned for further updates.

Workshop Chairs

barlet

Pere Barlet-Ros

BNN-UPC, Spain

Pedro.Casas

Pedro Casas

AIT, Austria

franco.scarselli

Franco Scarselli

University of Siena, Italy

Xiangle_Cheng

Xiangle Cheng

Huawei, China

cabellos

Albert Cabellos

BNN-UPC, Spain

Program 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
  • Dario Rossi, Huawei Technologies, France
  • Krzysztof Rusek, AGH University of Science and Technology, Poland
  • José Suárez-Varela. BNN-UPC, Spain
  • Stefano Traverso, Ermes Cyber Security, Italy