  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.
