Distributed Graph Learning with Smooth Data Priors
Abstract
Graph learning is often a necessary step in processing or representing structured data, when the underlying graph is not given explicitly. Graph learning is generally performed centrally with a full knowledge of the graph signals, namely the data that lives on the graph nodes. However, there are settings where data cannot be collected easily or only with a non-negligible communication cost. In such cases, distributed processing appears as a natural solution, where the data stays mostly local and all processing is performed among neighbours nodes on the communication graph. 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. We solve a distributed optimization problem with local projection constraints to infer a valid graph while limiting the communication costs. Our results show that the distributed approach has a lower communication cost than a centralised algorithm without compromising the accuracy in the inferred graph. It also scales better in communication costs with the increase of the network size, especially for sparse networks.
Cite
@article{arxiv.2112.05887,
title = {Distributed Graph Learning with Smooth Data Priors},
author = {Isabela Cunha Maia Nobre and Mireille El Gheche and Pascal Frossard},
journal= {arXiv preprint arXiv:2112.05887},
year = {2021}
}