Machine Learning in Network Centrality Measures: Tutorial and Outlook
This provides a more efficient solution for analyzing large real-world networks, though it is incremental as it applies existing machine learning techniques to a known bottleneck.
The paper tackles the high computational cost of centrality measures in complex networks by using neural networks to approximate these metrics, showing that the method is faster than other approximation algorithms.
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.