1 code implementation • 9 Sep 2021 • Hermina Petric Maretic, Mireille El Gheche, Giovanni Chierchia, Pascal Frossard
We tackle the problem of graph alignment by computing graph permutations that minimise our new filter distances, which implicitly solves the graph comparison problem.
no code implementations • 12 Mar 2020 • Hermina Petric Maretic, Mireille El Gheche, Matthias Minder, Giovanni Chierchia, Pascal Frossard
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 Laplacian matrices.
no code implementations • 20 Sep 2019 • Guillermo Ortiz-Jimenez, Mireille El Gheche, Effrosyni Simou, Hermina Petric Maretic, Pascal Frossard
Experiments show that the proposed method leads to a significant improvement in terms of speed and performance with respect to the state of the art for domain adaptation on a continually rotating distribution coming from the standard two moon dataset.
1 code implementation • NeurIPS 2019 • Hermina Petric Maretic, Mireille El Gheche, Giovanni Chierchia, Pascal Frossard
We present a novel framework based on optimal transport for the challenging problem of comparing graphs.
no code implementations • 24 Jan 2019 • Hermina Petric Maretic, Mireille El Gheche, Pascal Frossard
Graph inference methods have recently attracted a great interest from the scientific community, due to the large value they bring in data interpretation and analysis.
1 code implementation • 23 Oct 2018 • Hermina Petric Maretic, Pascal Frossard
Graph learning methods have recently been receiving increasing interest as means to infer structure in datasets.
1 code implementation • 18 Jul 2017 • Hermina Petric Maretic, Dorina Thanou, Pascal Frossard
If this is not possible, the data structure has to be inferred from the mere signal observations.