Semisupervised regression in latent structure networks on unknown manifolds

Semisupervised regression in latent structure networks on unknown manifolds

Abstract Random graphs are increasingly becoming objects of interest for modeling networks in a wide range of applications. Latent position random graph models posit that each node is associated with a latent position vector, and that these vectors follow some geometric structure in the latent space...

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Bibliographic Details
Main Authors: Aranyak Acharyya, Joshua Agterberg, Michael W. Trosset, Youngser Park, Carey E. Priebe
Format: Article
Language:English
Published: SpringerOpen 2023-11-01
Series:Applied Network Science
Subjects:
Network inference
Vertex covariates
Random dot product graph
Manifold learning
Regression
Online Access:https://doi.org/10.1007/s41109-023-00598-9

Internet

https://doi.org/10.1007/s41109-023-00598-9

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