  There are many networks in real life which exist as form of Scale-free
networks such as World Wide Web, protein-protein inter action network, semantic
networks, airline networks, interbank payment networks, etc. If we want to
analyze these networks, it is really necessary to understand the properties of
scale-free networks. By using the properties of scale free networks, we can
identify any type of anomalies in those networks. In this research, we proposed
a methodology in a form of an algorithm to predict hidden links and missing
nodes in scale-free networks where we combined a generator of random networks
as a source of train data, on one hand, with artificial neural networks for
supervised classification, on the other, we aimed at training the neural
networks to discriminate between different subtypes of scale-free networks and
predicted the missing nodes and hidden links among (present and missing) nodes
in a given scale-free network. We chose Bela Bollobas's directed scale-free
random graph generation algorithm as a generator of random networks to generate
a large set of scale-free network's data.
