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Identifiability of Latent Space Network Models on Anisotropic Thurston Geometries

US & West · 10 Sept

arXiv:2609.09236v1 Announce Type: new Abstract: A latent space network model places the nodes in a metric space and lets the probability of a tie decrease with distance. In a space of constant curvature, pairwise distances determine the positions up to an isometry.

Original headline: Identifiability of Latent Space Network Models on Anisotropic Thurston Geometries

Read the original: arXiv — Physics and Society (physics.soc-ph)

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Identifiability of Latent Space Network Models on Anisotropic Thurston Geometries

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