In this research, we propose a novel approach to model information diffusion on bibliographic networks using pretopology theory. We propose pretopological independent cascade model that is a variation of the independent cascade model (IC), namely \(Preto\_IC\) . We apply pretopology to model the structure of heterogeneous bibliographic networks since it is a powerful mathematical tool for complex network analysis. The highlights of \(Preto\_IC\) are that the propagation process is simulated on multiple relations, and the concept of elementary closed subset is applied to capture the seed set. In the first step, we construct a pretopological space to illustrate a heterogeneous bibliographic network. In this space, we define a strong pseudo-closure function to capture the neighborhood set of a set A. Next, we propose a new method to choose seed set based on the elementary closed subsets. Finally, we simulate \(Preto\_IC\) with the seed set from step (2) and for each step t of propagation, determine the neighborhood set for infection based on pseudo-closure function defined from step (1). We experiment on three real datasets and demonstrate the effectiveness of \(Preto\_IC\) compared with the IC model with existing methods of seed set selection.