Program

Week Thu. 09 Fri. 10 List

	Thu. 09	Fri. 10
09:00 10:00 11:00 12:00 13:00 14:00 15:00 16:00 17:00 18:00	Opening remarks 9:45 - 10:00 (15min) Opening remarks Introduction to the workshop (Invited talk) The stochastic blockmodel for clustering nodes in graphs and hypergraphs - Catherine Matias 10:00 - 11:00 (1h) (Invited talk) The stochastic blockmodel for clustering nodes in graphs and hypergraphs - Catherine Matias Building A - Plenary Room Catherine Matias https://statlearngraph23.sciencesconf.org/data/program/Matias_Abstract_Slides.pdf Contributed talks 11:00 - 12:00 (1h) Contributed talks Building A - Plenary Room › DIGRAC: Digraph Clustering Based on Flow Imbalance - Yixuan He, University of Oxford 11:00-11:20 (20min) › SSSNET: Semi-Supervised Signed Network Clustering - Yixuan He, University of Oxford 11:20-11:40 (20min) › Machine learning meets false discovery rate: application to graph anomaly detection - Ariane Marandon-Carlhian, Laboratoire de Probabilités, Statistique et Modélisation 11:40-12:00 (20min) Lunch break 12:00 - 13:30 (1h30) Lunch break (Invited talk) Opportunities and challenges of graph learning in biomedicine - Pietro Lio 13:30 - 14:30 (1h) (Invited talk) Opportunities and challenges of graph learning in biomedicine - Pietro Lio Building A - Plenary Room Pietro Lio https://statlearngraph23.sciencesconf.org/data/program/Pietro_Lio_talk.pdf Contributed talks 14:30 - 15:30 (1h) Contributed talks Building A - Plenary Room › Adding semantic to level-up graph-based Android malware detection - Roxane Cohen, Laboratoire dánalyse et modélisation de systèmes pour láide à la décision, CEntre de REcherches en MAthématiques de la DEcision 14:30-14:50 (20min) › Graph Neural Network go grammatical - Jason Piquenot, Laboratoire dÍnformatique Fondamentale et Appliquée de Tours, Laboratoire d'Informatique, du Traitement de l'Information et des Systèmes 14:50-15:10 (20min) › MSGNN: A Spectral Graph Neural Network Based on a Novel Magnetic Signed Laplacian - Yixuan He, University of Oxford 15:10-15:30 (20min) Coffee break 15:30 - 16:00 (30min) Coffee break Building A - Plenary Room (Invited talk) Learning distances for attributed graphs with optimal transport- Pierre Borgnat 16:00 - 17:00 (1h) (Invited talk) Learning distances for attributed graphs with optimal transport- Pierre Borgnat Building A - Plenary Room Pierre Borgnat https://statlearngraph23.sciencesconf.org/data/program/Pierre_Borgnat_Abstract_Slides.pdf Contributed talks 17:00 - 18:00 (1h) Contributed talks Building A - Plenary Room › Transductive Kernels for Gaussian Processes on Graphs - Yin-Cong Zhi, University of Oxford [Oxford] 17:00-17:20 (20min) › Learning Label Initialization for Time-Dependent Harmonic Extension - Amitoz Azad, Université de Caen Normandie 17:20-17:40 (20min) › Dynamic Ranking and Translation Synchronization - Eglantine Karlé, INRIA 17:40-18:00 (20min)	(Invited talk) Ranking from pairwise comparisons: a near-linear time minimax optimal algorithm for learning BTL weights - Julien Hendrickx 9:00 - 10:00 (1h) (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 10:00 - 10:30 (30min) Coffee break Building A - Plenary Room Contributed talks 10:30 - 11:30 (1h) 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 11:30 - 12:30 (1h) (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 12:30 - 14:00 (1h30) Lunch break (Invited talk) Learning in graphs with semi-definite programming: statistical bounds based on fixed point analysis and excess risk curvature - Stephane Chretien 14:00 - 15:00 (1h) (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 15:00 - 16:00 (1h) 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)

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