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Reinventing the Theory of Machine Learning on Graphs

CORDIS · observation · Publication date unknown

Reinventing the Theory of Machine Learning on Graphs In many scientic 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 scientic and industrial challenge. Graph Machine Learning (GML) is thus a fast-growing eld, with so-called Graph Neural Networks (GNN) at the forefront. However, in sharp contrast with traditional ML, the eld 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 signicant limitations both practical and theoretical, and it is not clear how to address them, and 2) GNNs do not take into account the specicities of graphs coming from domains as dierent as biolog

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award

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SIGNED

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EU

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1479643

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EUR