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.