2 citations · 2 across the 1 of their papers we have counts for
8 papers
Multilayer Graph Clustering with Optimized Node Embedding
Mireille El Gheche, Pascal Frossard
We are interested in multilayer graph clustering, which aims at dividing the graph nodes into categories or communities. To do so, we propose to learn a clustering-friendly embeddi…
FiGLearn: Filter and Graph Learning using Optimal Transport
Matthias Minder, Zahra Farsijani, Dhruti Shah +2
In many applications, a dataset can be considered as a set of observed signals that live on an unknown underlying graph structure. Some of these signals may be seen as white noise…
Multilayer Clustered Graph Learning
Mireille El Gheche, Pascal Frossard
Multilayer graphs are appealing mathematical tools for modeling multiple types of relationship in the data. In this paper, we aim at analyzing multilayer graphs by properly combini…
Wasserstein-based Graph Alignment
Hermina Petric Maretic, Mireille El Gheche, Matthias Minder +2
We propose a novel method for comparing non-aligned graphs of different sizes, based on the Wasserstein distance between graph signal distributions induced by the respective graph…
Joint Graph-based Depth Refinement and Normal Estimation
Mattia Rossi, Mireille El Gheche, Andreas Kuhn +1
Depth estimation is an essential component in understanding the 3D geometry of a scene, with numerous applications in urban and indoor settings. These scenes are characterized by a…
Forward-Backward Splitting for Optimal Transport based Problems
Guillermo Ortiz-Jimenez, Mireille El Gheche, Effrosyni Simou +2
Optimal transport aims to estimate a transportation plan that minimizes a displacement cost. This is realized by optimizing the scalar product between the sought plan and the given…