As the number and complexity of surrounding networks increases network science becomes more and more important. Network science is one of the fastest developing fields, its subjects among others include communicational, biological and social networks. In my thesis I aim to examine how information spreads on social networks and how the structure of the network influences it. My research is based on two of the leading theories in the field, on the viral model of Malcolm Gladwell and the cascade model of Duncan Watts. I ran my simulations in NetLogo, which is an agent-based modelling software. I tested various networks with random, clustered or scale-free characteristics to different degrees. I ran simulations on static and dynamically changing networks, paying attention particularly to the dynamic networks, because they describe the real world better. I found that the structure and clustering coefficient of the network is particularly important in the cascade model, whereas the density is crucial in both cases. There is difference between networks with growing and stagnating sizes, information spreads much more in a viral manner on growing networks. Finally I tried to give a few examples of practical application possibilities of the two models.