Searched for: subject%3A%22graph%255C%2Blearning%22
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Natali, A. (author), Isufi, E. (author), Coutino Minguez, M.A. (author)
This work proposes an algorithmic framework to learn time-varying graphs from online data. The generality offered by the framework renders it model-independent, i.e., it can be theoretically analyzed in its abstract formulation and then instantiated under a variety of model-dependent graph learning problems. This is possible by phrasing (time...
article 2022
Natali, A. (author), Isufi, E. (author), Coutino, M. (author), Leus, G. (author)
Topology identification is an important problem across many disciplines, since it reveals pairwise interactions among entities and can be used to interpret graph data. In many scenarios, however, this (unknown) topology is time-varying, rendering the problem even harder. In this paper, we focus on a time-varying version of the structural...
conference paper 2021