Relational or non-euclidean data such as physical or social networks, point clouds or biological components can often be described with underlying graphs. Graphs are a key concept invarious disciplines such as computer science, social science, or medicine, as well as an incredibly rich mathematical field. It is therefore natural that they form the core of the work of an increasingly large group of researchers from machine learning, statistics, signal processing and optimization.

These communities rarely share the same venues. This conference aims to facilitate new collaborations across geographical boundaries but also to explore synergies across research topics. World-class researchers will be invited to present state-of-the-art works along with young researchers (early careers, PhD students) to promote and develop new mentorship.

The program will revolve around three major themes. First, the recent impact of Graph Neural Networks on various task of machine learning on graphs such as graph generation, representation learning, graph embedding, as well as their current limitations, and how to overcome them. Second, how statistical properties of random graphs can be exploited in learning theory, and what kind of asymptotic and non-asymptotic behaviour we can expect for static and dynamic graphs. Finally, how new optimization methods can help graph signal processing, including non-smooth and distributed methods.