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

Predicting Hidden Links and Missing Nodes in Scale-Free Networks with Artificial Neural Networks