Graph sampling for node embedding

Li-Chun Zhang

Graph sampling for node embedding

Machine Learning 2022-10-20 v1 Machine Learning

Authors: Li-Chun Zhang

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.

Keywords

graph and network analysis sampling algorithms manifold learning

Cite

@article{arxiv.2210.10520,
  title  = {Graph sampling for node embedding},
  author = {Li-Chun Zhang},
  journal= {arXiv preprint arXiv:2210.10520},
  year   = {2022}
}

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