Statistical learning on large scale graphs

(Invited talk) Ranking from pairwise comparisons: a near-linear time minimax optimal algorithm for learning BTL weights - Julien Hendrickx

Building A - Plenary Room

Julien Hendrickx

https://statlearngraph23.sciencesconf.org/data/program/Julien_abstract_slides.pdf

Coffee break

Building A - Plenary Room

Contributed talks

Building A - Plenary Room

› Curvature-GAT : Improve graph attention with local structural information - Stephane Chretien, Université Lyon 2 10:30-10:50 (20min)

› Non Parametric and Semi-Parametric Modeling of a Sequence of Graphs for Testing Abnormality: Application to Cybersecurity - Clarisse Boinay, Inria Lille - Nord Europe 10:50-11:10 (20min)

› Trust and scalability through randomized communication graphs - Jan Ramon, INRIA-Lille 11:10-11:30 (20min)

(Invited talk) Graph matching: from fundamental limits to algorithms - Marc Lelarge

Building A - Plenary Room

Marc Lelarge

https://statlearngraph23.sciencesconf.org/data/program/Lelarge_Abstract_Slides.pdf

Lunch break

(Invited talk) Learning in graphs with semi-definite programming: statistical bounds based on fixed point analysis and excess risk curvature - Stephane Chretien

Building A - Plenary Room

Stephane Chretien

https://statlearngraph23.sciencesconf.org/data/program/Stephane_Chretien_Abstract_Slides.pdf

Contributed talks

Building A - Plenary Room

› Graph Neural Networks and Optimal Transport for Distance Learning between Graphs - Aldo Moscatelli, Laboratoire d'Informatique, du Traitement de l'Information et des Systèmes 15:00-15:20 (20min)

› Large population limits of Markov processes on random networks - Marvin Lücke, Zuse Institute Berlin 15:20-15:40 (20min)

› Temporal network compression via network hashing - Rémi Vaudaine, Réseaux dynamiques : approche structurelle et temporelle 15:40-16:00 (20min)

Program