Identifying influential users and measure the influence of nodes in social networks have become an interesting and important topic of research. It is crucial to find out to what extent individuals influence each other because it can be used to control rumors, diseases, and diffusion. There are numerous relevant models most of which are based on a homogeneous network. However, in the real world, we face heterogeneous networks where the nodes and edges are different types. A network is homogeneous if and only if the edges and nodes are of the same type, and it is considered heterogeneous if the nodes and edges are different. In heterogeneous networks, there is a concept known as meta-path, which indicates the type of communication between two nodes. In this paper, we aim to locate influential nodes by calculating the entropy of different meta-paths. To evaluate information diffusion in a heterogeneous network, we used the known susceptible-infectious model. The results of our experiments on three real-world networks’ dataset show that the proposed method outperforms state-of-the-art influence maximization algorithms.