Graph sampling for node embedding
Not on the map. Why?
Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 7 of its 17 references that are in the map. First posted on arXiv 19 Oct 2022.
Builds on
7 of 17 references.
References · 7
- 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,361
- 2018Graph Neural Networks: A Review of Methods and Applicationscited by 1,576
- 2014DeepWalk: Online Learning of Social Representationscited by 1,547
- 2017Cooperative Game Theory Approaches for Network Partitioningcited by 135
- 2022A Survey on Graph Representation Learning Methodscited by 66
- 2016Random walks and diffusion on networkscited by 28
- 2022Graph spatial samplingcited by 1
Built on by
Not cited yet.