Fractal Modeling of Big Data Networks

In recent years, the key role of complex and big data networks in different areas of sciences have conducted many studies to analyze their structures and functions and to understand these systems deeply. Since the classical network models used to indicate complex networks do not capture their main features such as centrality, clustering, degree distribution, etc. several attempts have been made to introduce new network models with desired features. In this paper, a novel method is presented based on fractal theory for modeling of big data networks. It is capable of modeling different complex networks. It is based on a mapping of the adjacency matrix into an nD space. In fact, it is a self-similarity network evolving the model based on the similarity on an adjacency matrix. We then show that the model has the expected properties and it can actually be seen as a general model for complex networks. Therefore, this model can be used to classify and to cluster data and to predict events in complex networks.