Autor:	Zhang, Li-Chun
Rok vydání:	2022
Předmět:	Statistics - Machine Learning Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	Node embedding is a central topic in graph representation learning. Computational efficiency and scalability can be challenging to any method that requires full-graph operations. We propose sampling approaches to node embedding, with or without explicit modelling of the feature vector, which aim to extract useful information from both the eigenvectors related to the graph Laplacien and the given values associated with the graph.
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2210.10520 Zobrazit plný text záznamu View this record from Arxiv