To date, research on relation mining has typically focused on analyzing explicit relationships between entities, while ignoring the underlying connections between entities, known as implicit relationships. Exploring implicit relationships can reveal more about social dynamics and potential relationships in heterogeneous social networks to better explain complex social behaviors. The research presented in this paper explores implicit relationships discovery methods in the context of heterogeneous social networks. First, the creation of a novel implicit relationships dataset is described, namely HIMdata. Next, a framework for discovering implicit relationships in heterogeneous social networks is introduced. The proposed framework, HIM, innovatively integrates node attributes information and network structure information with graph convolutional networks for discovery of implicit relationships. Finally, HIM is evaluated on two different types of networks, achieving state-of-the-art performance on implicit relationship discovery tasks. The source codes are released at https://github.com/myjpgit/HIM.git.