Gradient-based spectral embeddings of Random Dot Product Graphs.

The Random Dot Product Graph (RDPG) is a generative model for relational data, where nodes are represented via latent vectors in low-dimensional Euclidean space. RDPGs crucially postulate that edge formation probabilities are given by the dot product of the corresponding latent positions. Accordingl...

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Bibliographic Details

Main Author:	Fiori, Marcelo (author)
Other Authors:	Marenco, Bernardo (author), Larroca, Federico (author), Bermolen, Paola (author), Mateos, Gonzalo (author)
Format:	article
Language:	English
Published:	2024
Subjects:	Graph representation learning Gradient descent Manifold optimization Random dot product graphs Optimization Information processing Matrix decomposition Manifolds Representation learning Data models Scalability
Online Access:	https://hdl.handle.net/20.500.12008/47590
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