Reserving-Masking-Reconstruction Model for Self-Supervised Heterogeneous Graph Representation

Self-supervised Heterogeneous Graph Representation (SSHGRL) learning is widely used in data mining. The latest SSHGRL methods normally use metapaths to describe the heterogeneous information (multiple relations and node types) to learn the heterogeneous graph representation and achieve impressive results. However, establishing metapaths requires lofty computational costs that are too high for the medium and large graphs. To this end, this paper proposes a Reserving-Masking-Reconstruction (RMR) model that can fully consider heterogeneous information without relying on the metapaths. In detail, we propose a reserving method to reserve to-be-masked nodes' (target nodes) information before graph masking. Second, we split the reserved graph into relation subgraphs according to the type of relations that require much less computational overheads than metapath. Then, the target nodes in each relation subgraph are randomly masked with minimal topology information loss. After, a novel reconstruction method is proposed to reconstruct the masked nodes on different relation subgraphs to establish the self-supervised signal. The proposed method requires low computational complexity and can establish a self-supervised signal without deeply changing the graph topology. Experimental results show the proposed method achieves state-of-the-art records on medium and large-scale heterogeneous graphs and competitive records on small-scale heterogeneous graphs. The code is available at https://github.com/DuanhaoranCC/RMR.