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GNNet@CoNEXT 2023 : Graph Neural Networking Workshop (co-located with ACM CoNEXT 2023)

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Link: https://bnn.upc.edu/workshops/gnnet2023/
 
When Dec 8, 2023 - Dec 8, 2023
Where Paris
Submission Deadline Sep 30, 2023
Notification Due Oct 21, 2023
Final Version Due Oct 25, 2023
Categories    networking   graph neural networks   machine learning   communications
 

Call For Papers

2nd GNNet Workshop
Graph Neural Networking Workshop
Co-located with ACM CoNEXT 2023
December 8, 2023
https://bnn.upc.edu/workshops/gnnet2023/


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.

All accepted papers will be included in the conference proceedings and be made available in the ACM Digital Library.


SPECIAL SESSION
===============

GNNet will include a dedicated special session where the top teams competing at the fourth edition of the Graph Neural Networking Challenge (https://bnn.upc.edu/challenge/gnnet2023/) will be invited to present the winning solutions of the challenge, providing an excellent complement to the main program.


IMPORTANT DATES
===============

Paper submission deadline: September 30, 2023 (extended – HARD DEADLINE)
Paper acceptance notifications: October 21, 2023 (extended)
Camera ready due: October 25, 2023


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.

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


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-gnnet2023.hotcrp.com


WORKSHOP CHAIRS
===============

- Pere Barlet-Ros, BNN-UPC, Spain
- Pedro Casas, AIT, Austria
- Franco Scarselli, University of Siena, Italy
- José Suárez-Varela, Telefónica Research, Spain
- Albert Cabellos, BNN-UPC, Spain

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