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.

Mahsa Asadi

Apprentissage et graphiques décentralisés

Onkar Pandit

Graph-based Machine Learning for Linguistic Structure Prediction

Arijus Pleska

Tractable probabiistic models for large scale networks

César rufino Sabater

Privacy Preserving Machine Learning

Brij Mohan Lal Srivastava

Decentralized Machine Learning under Constraints

Mariana Vargas Vieyra

Adaptive Graph Learning with Applications to Natural Language Processing

Géraud Le Falher

Characterizing edges in signed and vector-valued graphs 2018-04-16

David Chatel

Semi-supervised clustering in graphs 2017-12-07

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

Other ' DatInG : Data Intelligence Group ' teams