Search Results for author:
Found 7 papers, 4 papers with code
GOT: An Optimal Transport framework for Graph comparison
We present a novel framework based on optimal transport for the challenging problem of comparing graphs.
FGOT: Graph Distances based on Filters and Optimal Transport
We tackle the problem of graph alignment by computing graph permutations that minimise our new filter distances, which implicitly solves the graph comparison problem.
Graph learning under sparsity priors
If this is not possible, the data structure has to be inferred from the mere signal observations.
Graph Laplacian mixture model
Graph learning methods have recently been receiving increasing interest as means to infer structure in datasets.
Graph heat mixture model learning
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.
Forward-Backward Splitting for Optimal Transport based Problems
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.
Wasserstein-based Graph Alignment
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.
