Graph sampling for node embedding

• URL: http://arxiv.org/abs/2210.10520v1
• Date: Wed, 19 Oct 2022 12:54:37 GMT
• Title: Graph sampling for node embedding
• Authors: Li-Chun Zhang
• Abstract summary: We propose sampling approaches to node embedding, with or without explicit modelling of the feature vector. We aim to extract useful information from both the eigenvectors related to the graph Laplacien and the given values associated with the graph.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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