In numerous networks, it is vital to identify communities consisting of closely joined groups of individuals. Such communities often reveal the role of the networks or primary properties of the individuals. In this perspective, Newman and Girvan proposed a modularity score (Q) for quantifying the power of community structure and measuring the appropriateness of a division. The Q function has newly become a significant standard. In this paper, the strengths of the Q score and another technique known as the divisive algorithm are combined to enhance the efficiently of the identification of communities from a network. To achieve that goal, we have developed a new algorithm. The simulation results indicated that our algorithm achieved a division with a slightly higher Q score against some conventional methods.