Assessing the reliability of complex networks: empirical models based on machine learning

In this paper three models derived using Machine Learning techniques (Support Vector Machines, Decision Trees and Shadow Clustering) are compared for approximating the reliability of real complex networks, such as for water supply, electric power or gas distribution systems or telephone systems, using different reliability criteria.