Keriven Lab

Research focus

ERC Starting Grant · 2024

Reinventing the Theory of Machine Learning on Graphs

In many scientific domains, graphs are the objects of choice to represent structured data: from molecules to social networks, power grids, the internet, and so on. The exploitation of graph data represents a major scientific and industrial challenge. Graph Machine Learning (GML) is thus a fast-growing field, with so-called Graph Neural Networks (GNN) at the forefront. However, in sharp contrast with traditional ML, the field of GML has somewhat jumped from early methods to deep learning, without the decades-long development of well- established notions to compare, analyze and improve algorithms. As a result, 1) GNNs, all based on the so-called message-passing paradigm, have significant…

From the public funding record at EU CORDIS. Describes the funded project, not the reviews below.