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Found 12 papers, 2 papers with code
Distributed Graph Learning with Smooth Data Priors
We propose here a novel distributed graph learning algorithm, which permits to infer a graph from signal observations on the nodes under the assumption that the data is smooth on the target graph.
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.
Multilayer Graph Clustering with Optimized Node Embedding
To do so, we propose to learn a clustering-friendly embedding of the graph nodes by solving an optimization problem that involves a fidelity term to the layers of a given multilayer graph, and a regularization on the (single-layer) graph induced by the embedding.
Multilayer Clustered Graph Learning
In this paper, we aim at analyzing multilayer graphs by properly combining the information provided by individual layers, while preserving the specific structure that allows us to eventually identify communities or clusters that are crucial in the analysis of graph data.
FiGLearn: Filter and Graph Learning using Optimal Transport
We cast a new optimisation problem that minimises the Wasserstein distance between the distribution of the signal observations and the filtered signal distribution model.
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.
Joint Graph-based Depth Refinement and Normal Estimation
Depth estimation is an essential component in understanding the 3D geometry of a scene, with numerous applications in urban and indoor settings.
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.
GOT: An Optimal Transport framework for Graph comparison
We present a novel framework based on optimal transport for the challenging problem of comparing graphs.
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.
Stochastic Gradient Descent for Spectral Embedding with Implicit Orthogonality Constraint
In this paper, we propose a scalable algorithm for spectral embedding.
OrthoNet: Multilayer Network Data Clustering
We propose in this paper to extend the node clustering problem, that commonly considers only the network information, to a problem where both the network information and the node features are considered together for learning a clustering-friendly representation of the feature space.
