In sociology, group dynamics is important in understanding how people make decisions according to different group settings and conditions. Opinion dynamics shows how likely people are to agree with one another based on their mutual influences, which shape the opinion formations that take place among them. In engineering, opinion dynamics is important when dealing with decentralized network systems in which there is not one central unit, but rather a collective behavior between many distributed units that make up that network. Our aim is to understand how different variations to the existing models of opinion dynamics can affect different transient behaviors on the convergence of opinion and whether these opinions ultimately converge to consensus. Using the DeGroot and Friedkin-Johnsen models, coupled with MATLAB simulations, we were able to make such variations. Our results show that particular conditions over the network topology and the weights of the influences among the agents can lead to different degrees of consensus within the entire network or among different groups in the network. These particular conditions also show predictable behavior over the convergence rate of different opinions.