In this post, we will look at how a Graph Neural Network can be deployed to approximate network centrality measures, such as Harmonic centrality, Eigenvector centrality, etc. and include them as properties in a Neo4j graph. — A common challenge graph analysts face is the time complexity constraints many of the most important centrality metrics have. For instance, Cohen et al. illustrate in “Computing Classic Closeness Centrality, at Scale”, that calculating the closeness centrality on a network of 24 million nodes takes an astonishing 44,222 hours, or…