On Approximating Networks Centrality Measures via Neural Learning Algorithms

The analysis and study of complex networks are crucial to a number of applications. Vertex centrality measures are an important analysis mechanism to uncover or rank important elements of a given network. However, these metrics have high space and time complexity, which is a severe problem in applications that typically involve large networks. We propose and study the use of neural learning algorithms in such a way that the use of these metrics became feasible in networks of any size. We trained and tested 12 off-the-shelf learning algorithms on several networks. Our results show that the regression output of the machine learning algorithms successfully approximate the real metric values and are a robust alternative in real world applications. We also identified that the model generated by the multilayer layer network trained with the Levenberg-Marquardt algorithm achieved the best performance, both in process time and solution quality, among all the methodologies tested for this task.