A Novel Fractal Model for Analyzing Complex Networks

Owing to the importance of complex networks, in recent years, many studies have been conducted on these networks in order to analyze their structures and functions, since they play a crucial role in different sciences. Complex systems are composed of many units, by an important role and special functions. Owing to the intricate structure of networks is a network representation in which the nodes correspond to the other nodes of other units or the same unit. In recent years, many researchers have concentrated on generating networks with desired properties. To construct networks with realistic features, we introduce a new approach capable of generating different network types prescribed statistical properties and it can be used as a model of actual data. It is based on a mapping of the adjacency matrix in a 2D or 3D space. One of the important features is that the network topology structure becomes more accurate by increasing the size of the system. In fact, it is a self-similarity network evolving the model based on the attribute similarity on an adjacency matrix. As regards, each node has a value, then adjacent nodes have similar attribute and fall in a region. Therefore, scientists in different fields (such as computer science, electronic or complex systems) can use this self-similar structure allowing them to generate their desired network model. Furthermore, classifying and clustering data in networks are one of the most important features of this method.