Contagion processes arise broadly in the social and biological sciences, manifested as, for example the spread of infectious and the diffusion of innovations. Depending on the network structure, the transmission dynamics can have different. However, when the structure is too complex (e.g. multipartite networks), understanding the properties of the network might not be sufficient to predict and compare the transmission dynamics between similar networks. We developed an algorithm that systematically compares the transmission dynamic trajectories of different networks. Our method not only compare the final results of the transmission, but the transmission trend over time by using entire temporal simulation results. Our method can indicate that the transmission dynamics of a network is similar or dissimilar to the reference network. The algorithm addresses the problem of only comparing the end results; when the behavior of transmission might be completely different between networks, but final number of infection is the same. For highly stochastic processes, the algorithm may deduce higher dissimilarities due to the variable possibilities in dynamics. This can be overcome by determining whether the reference network show high dissimilarity to itself. By investigating the entire parameter space of transmission model that is being used, the similarity trend can be determined for both the reference network and comparing network. The method needs sufficient amount of reproduced data (in our experiment N = 1000). Comparing empirical transmission dynamics between two networks would be a challenge because empirical data are not as readily reproducible as computational simulations.