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

Linyu Li

Abstract:

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.