In recent years, the need to accommodate non-regular structures in data science has brought a boom in machine learning methods on graphs. Graph deep learning (GDL) has made groundbreaking achievements in the applied sciences and was adapted as a general-purpose tool in many industries. These achievements pose exciting theoretical challenges: can the success of GDL be grounded in solid mathematical frameworks? Our research aims at developing new methods and mathematical foundations for GDL.
