Synchronization phenomena in large populations of interacting elements are the subject of intense research efforts in physical, biological, chemical, and social systems. Coupled biological and chemical systems, neural networks, social interacting species, the Internet and the World Wide Web, are only a few examples of systems composed by a large number of highly interconnected dynamical units. The first approach to capture the global properties of such systems is to model them as graphs whose nodes represent the dynamical units, and whose links stand for the interactions between them. A successful approach to the problem of synchronization consists of modeling each member of the population as a phase oscillator. In this study , synchronization is analyzed in one of the most representative models of coupled phase oscillators, the Kuramoto mode l, a mathematical model that speaks to the very nature of coupled oscillating processes. A rigorous mathematical treatment, specific numerical methods, and many variations and extensions of the original model have appeared in the last few years are presented. Relevant applications of the model in different contexts are also included. In this study, we first provide a description about different types of network, and the method to build them. Then we introduce and describe synchronization, kuramoto model, the order parameter and correlation matrix . After that we study the impact of the natural frequency of bimodal distribution on the kuramoto model for small-world network, previously shown that there are numerous solutions for it. We will show that taking bimodal distribution of natural frequency, in the cause of unmodel frequency distribution, in specific frequency ?= 0.9 ,?= 1 $ ) the order parameter will no longer be stationary and different states will be observed . This phenomena is similar to what happens in pacemaker cells. And the defects of network are changed or removed. We need more time to reach the stationary state , when the coupling constant is rescaled by degree of node. Also, we see that r is periodic in smaller frequencies ( ? = 0.09 ,?= 0. 1 ).When we rescale the coupling constant, network become more sensitive to change ?. We can say that this different behavior of small world network is because of its two main features : high clustering coefficient and small shortest path length. Also by rescaling kuramoto model for scale-free and random networks, it has been observed that the order parameter will no longer shows at stationary state in frequencies which are lower than the frequencies of not rescaled states. With regard to this fact that small-world network has two main characteristics of high clustering coefficient and small shortest path length, it shows different behavior from random and scale-free networks. At last, we apply normal and bimodal and Gaussian distribution on small-world, random and scale-free networks for two situation of rescaled and not rescaled kuramoto model. Also synchronized points will be determined through sketching correlation matrix