Similarity between Hypergraphs Based on Mathematical Morphology. - Isabelle Bloch; Alain Bretto; Aurélie Leborgne (ISMM, 2013)

Extracting Noise-Resistant Skeleton on Digital Shapes for Graph Matching. - Aurélie Leborgne; Julien Mille; Laure Tougne (ISVC (1), 2014)

Robust similarity between hypergraphs based on valuations and mathematical morphology operators. - Isabelle Bloch; Alain Bretto; Aurélie Leborgne (Discret. Appl. Math., 2015)

Noise-resistant Digital Euclidean Connected Skeleton for graph-based shape matching. - Aurélie Leborgne; Julien Mille; Laure Tougne (J. Vis. Commun. Image Represent., 2015)

Robust Computations of Reeb Graphs in 2-D Binary Images. - Antoine Vacavant; Aurélie Leborgne (CTIC, 2016)

Hierarchical skeleton for shape matching. - Aurélie Leborgne; Julien Mille; Laure Tougne (ICIP, 2016)

Appariement de formes basé sur une squelettisation hiérarchique. (Shape matching based on a hierarchical skeletonization). - Aurélie Leborgne (2016)

Medical Image Processing and Numerical Simulation for Digital Hepatic Parenchymal Blood Flow. - Marie-Ange Lèbre; Khaled Arrouk; Anh-Khoa Võ Van; Aurélie Leborgne; Manuel Grand-Brochier; Pierre Beaurepaire; Antoine Vacavant; Benoît Magnin; Armand Abergel; Pascal Chabrot (SASHIMI@MICCAI, 2017)

Euclidean Distance-Based Skeletons: A Few Notes on Average Outward Flux and Ridgeness. - Julien Mille; Aurélie Leborgne; Laure Tougne (J. Math. Imaging Vis., 2019)

Analyse de la connectivité fonctionnelle cérébrale de modèles murins par extraction de motifs fréquents dans des graphes. - Aurélie Leborgne; Florence Le Ber; Laetitia Degiorgis; Jules Massart; Laura Harsan; Stella Marc-Zwecker; Vincent Noblet (EGC, 2021)

Random Generation of a Locally Consistent Spatio-Temporal Graph. - Aurélie Leborgne; Marija Kirandjiska; Florence Le Ber (ICCS, 2021)

Analysis Of Brain Functional Connectivity By Frequent Pattern Mining In Graphs. Application To The Characterization Of Murine Models. - Aurélie Leborgne; Florence Le Ber; Laetitia Degiorgis; Laura Harsan; Stella Marc-Zwecker; Vincent Noblet (ISBI, 2021)

Mining frequent approximate patterns in large networks. - Kaouthar Driss; Wadii Boulila; Aurélie Leborgne; Pierre Gançarski (Int. J. Imaging Syst. Technol., 2021)

Recherche de motifs fréquents dans des graphes spatio-temporels. - Aurélie Leborgne; Ezriel Steinberg; Laurine Lafontaine; Florence Le Ber; Antoine Vacavant (EGC, 2022)

Resolution methods for constraint satisfaction problem in remote sensing field: A survey of static and dynamic algorithms. - Zouhayra Ayadi; Wadii Boulila; Imed Riadh Farah; Aurélie Leborgne; Pierre Gançarski (Ecol. Informatics, 2022)

Multi-SPMiner: A Deep Learning Framework for Multi-Graph Frequent Pattern Mining with Application to spatiotemporal Graphs. - Assaad Oussama Zeghina; Aurélie Leborgne; Florence Le Ber; Antoine Vacavant (KES, 2023)

Multi-scale Component-Tree: A Hierarchical Representation for Sparse Objects. - Romain Perrin; Aurélie Leborgne; Nicolas Passat; Benoît Naegel; Cédric Wemmert (DGMM, 2024)

Multi-SPMiner : un framework d'apprentissage profond pour l'extraction de motifs fréquents dans des multi-graphes avec application aux graphes spatio-temporels. - Assaad Oussama Zeghina; Aurélie Leborgne; Florence Le Ber; Antoine Vacavant (EGC, 2024)

Multi-Scale Component-Trees for Enhanced Representation in Multiplex Immunohistochemistry Imaging. - Romain Perrin; Aurélie Leborgne; Nicolas Passat; Benoît Naegel; Cédric Wemmert (ISBI, 2024)

Deep learning on spatiotemporal graphs: A systematic review, methodological landscape, and research opportunities. - Assaad Oussama Zeghina; Aurélie Leborgne; Florence Le Ber; Antoine Vacavant (Neurocomputing, 2024)

Deep QMiner: Towards a Generalized Deep Q-Learning Approach for Graph Pattern Mining. - Assaad Oussama Zeghina; Aurélie Leborgne; Florence Le Ber; Antoine Vacavant (GbRPR, 2025)