# Node Centralities and Classification Performance for Characterizing Node Embedding Algorithms

arXiv: https://arxiv.org/abs/1802.06368 · HTML: https://mlsanity.com/paper/1802.06368/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Feb 2018.

## Built on by (most cited first, 1 of 1)

- [Information Geometry of Absorbing Markov-Chain and Discriminative Random Walks](https://mlsanity.com/paper/2602.08185/) (2026)

## Abstract (arXiv)

Embedding graph nodes into a vector space can allow the use of machine learning to e.g. predict node classes, but the study of node embedding algorithms is immature compared to the natural language processing field because of a diverse nature of graphs. We examine the performance of node embedding algorithms with respect to graph centrality measures that characterize diverse graphs, through systematic experiments with four node embedding algorithms, four or five graph centralities, and six datasets. Experimental results give insights into the properties of node embedding algorithms, which can be a basis for further research on this topic.