Leader: Marc Tommasi



Magnet is a joint team between the University of Lille within the CRIStAL and Inria Lille Nord Europe. The Magnet project aims to design new machine learning based methods geared towards mining information networks. Information networks are large collections of interconnected data and documents like citation networks and blog networks among others. For this, we will define new structured prediction methods for (networks of) texts based on machine learning algorithms in graphs. Such algorithms include node classification, link prediction, clustering and probabilistic modeling of graphs. Envisioned applications include browsing, monitoring and recommender systems, and more broadly information extraction in information networks. Application domains cover social networks for cultural data and e-commerce, and biomedical informatics.

Research themes

Specifically, our main objectives are :

  • Learning graphs, that is graph construction, completion and representation from data and from networks (of texts)
  • Learning with graphs, that is the development of innovative techniques for link and structure prediction at various levels of (text) representation.

Each item will also be studied in contexts where little (if any) supervision is available. Therefore, semi-supervised and unsupervised learning will be considered throughout the project.



  • Professors
    • Rémi Gilleron
    • Marc Tommasi (Responsable)
  • Research director
    • Jan Ramon
  • Associate professors
    • Mikaela Keller
    • Fabien Torre
    • Fabio Vitale
  • Research scientists
    • Aurélien Bellet
    • Pascal Denis


  • Phd students
    • Mathieu Dehouck
    • Géraud Le Falher
    • Thibault Lietard

David Chatel

Semi-supervised graph clustering and information diffusion

Mathieu Dehouck

Graph-based Learning for Multi-lingual and Multi-domain Dependency Parsing

Géraud Le Falher

Machine Learning Algorithms on Signed Graphs for Link Ranking and Classification

Thibault Lietard

Adaptative Graph Learning with Applications to Natural Language Processing

Pauline Wauquier

Task driven representation learning 2017-05-29

Thomas Ricatte

Hypernode Graphs For Learning From Binary Relations Between Sets of Objects 2015-01-23

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