Offre en lien avec l’Action/le Réseau : – — –/– — –

Laboratoire/Entreprise : LISIC – Univ-Littoral
Durée : 6 months
Contact : esteban.bautista@univ-littoral.fr
Date limite de publication : 2025-03-01

Contexte :
Temporal graphs, representing interactions over time, are crucial for analyzing datasets in areas like Industry 4.0, cybersecurity, and social networks. Temporal graphs often exhibit periods of distinct activity regimes, making change point (CP) detection vital for tasks such as fault detection and prediction. However, the sparsity and irregularity of real-world temporal graphs make CP detection highly challenging, as current algorithms struggle to extract accurate patterns.

Sujet :
The internship aims to build upon recent works that allow to transform temporal graphs upon a spectral domain where comparing different periods of a temporal graph is easier. Yet such transform relies on the choose of graph and signal dictionaries that must be properly chosen to attain satisfactory detection accuracy. For signal dictionaries, we aim to compare different choices, like Haar, Walsh, and Boolean-based dictionaries, which are adapted to the binary and sparse nature of temporal graphs. For graph dictionaries, we aim to build custom dictionaries with user-defined motifs.

Profil du candidat :
This internship is directed at students with various backgrounds (computer science, data science, signal processing,
complex systems) but with a strong interest in data science and graphs. Interest in the theoretical aspects of machine learning and in Python development will a plus.

Formation et compétences requises :
Ing3 or M2 students

Adresse d’emploi :
Saint Omer, France

Document attaché : 202412060909_Data2Laws___M2_Internship.pdf