  Complex networks are ubiquitous to several Computer Science domains.
Centrality measures are an important analysis mechanism to uncover vital
elements of complex networks. However, these metrics have high computational
costs and requirements that hinder their applications in large real-world
networks. In this tutorial, we explain how the use of neural network learning
algorithms can render the application of the metrics in complex networks of
arbitrary size. Moreover, the tutorial describes how to identify the best
configuration for neural network training and learning such for tasks, besides
presenting an easy way to generate and acquire training data. We do so by means
of a general methodology, using complex network models adaptable to any
application. We show that a regression model generated by the neural network
successfully approximates the metric values and therefore are a robust,
effective alternative in real-world applications. The methodology and proposed
machine learning model use only a fraction of time with respect to other
approximation algorithms, which is crucial in complex network applications.
