| Sumario: | 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. The GNNet workshop provides the first dedicated venue to present and discuss the latest advancements on the emerging topic of GNNs applied to computer networking problems. GNNet brings together leaders from academia and industry to showcase recent methodological advances of GNNs and their application to computer networks, covering a wide range of applications and practical challenges for large-scale training and deployment. The GNNet workshop serves as the meeting point for the growing community on this fascinating domain, which previously did not have a specific forum for sharing ideas and discussion.
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