Description du sujet de thèse

Most theoretical works in Graph ML still relies on classic graph theory, where the graph structure is the primary object of interest. However, it has now become clear that node features are an equally important part for prediction, and the relationship between graph structure and node features plays a key role that is still poorly understood. However, the incorporation of node features in graph ML remains largely open, both in empirical benchmarks and in terms of modelization and theoretical understanding. In particular, statistical models of random graphs, which are crucial in Graph ML theory to characterize properties of learnability and generalization, often ignore node features altogether or are limited to toy models.

The goal of this PhD thesis, is to establish new models in Graph ML that will naturally incorporate node features and graph structure together. This will include a strong benchmarking of different learning algorithms and datasets to better characterize the role of node features, followed by the definition of new statistical models of graphs that include node features and their study, which will illuminate key properties of real-world datasets and provide inspiration for new GNN architectures. The candidate should have a background in Machine Learning and Computer Science. The balance between theoretical and empirical studies may depend on the candidate.

Contexte de travail

The candidate will be attached to IRISA's COMPACT team.

About the laboratory
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www.irisa.fr
IRISA is one of France's largest research laboratories (over 850 people) in the field of computer science and information technology. Structured into seven scientific departments, IRISA is a laboratory of excellence whose scientific priorities are bioinformatics, systems security, new software architectures, virtual reality, Big Data analysis and artificial intelligence. With its sights set on the future of information technology and its international outlook, IRISA is at the heart of society's digital transition and innovation in the fields of cybersecurity, health, environment and ecology, transport, robotics, energy, culture and artificial intelligence.

Presentation of CNRS as an employer: https://www.cnrs.fr/fr/le-cnrs
IRISA as laboratory of assignment: https://www.irisa.fr/umr-6074

Contraintes et risques

The position is located in a sector covered by the protection of scientific and technical potential (PPST), and therefore requires, in accordance with regulations, that your arrival be authorized by the competent authority of the MESR.

Informations complémentaires

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