Self-supervised heterogeneous graph representation learning (SSHGRL) is a key technique for embedding heterogeneous graphs, enabling effective analysis and modeling of social networks and other graph-structured data, which are central to knowledge discovery and the study of social systems. However, existing SSHGRL methods are hardly applied to large-scale heterogeneous graph environments due to the normally-used metapath decomposing mechanism being graph-size-sensitive. Moreover, the existing self-supervised signals are normally created from Shared Mutual Information (SMI) of different graph views that ignore the Non-shared Mutual Information (NMI) contained in the same view. This results in the model tending to learn insufficient graph representation. To this end, this paper proposes a designated masking propagation (DMP) mechanism to process heterogeneous graphs without using metapath. Moreover, based on the DMP graph view, a novel sufficient representation is proposed to learn the effective graph representation by combining both NMI and SMI. Extensive experiments on eight large- and medium-scale heterogeneous graph datasets demonstrate the superiority of our method, setting new state-of-the-art performance in various big data contexts.
Duan et al. (Mon,) studied this question.