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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.
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.
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