Estimating complex networks centrality via neural networks and machine learning

Abstract

Vertex centrality measures are important analysis elements in complex networks and systems. These metrics have high space and time complexity, which is a severe problem in applications that typically involve large networks. To apply such high complexity metrics in large networks we trained and tested off-the-shelf machine learning algorithms on several generated networks using five well-known complex network models. Our main hypothesis is that if one uses low complexity metrics as inputs to train the algorithms, one will achieve good approximations of high complexity measures. Our results show that the regression output of the machine learning algorithms applied in our experiments successfully approximate the real metric values and are a robust alternative in real world applications, in particular in complex and social network analysis.