Self-supervised Heterogeneous Graph Variational Autoencoders
Yige Zhao Jianxiang Yu Yao Cheng Chengcheng Yu Yiding Liu Xiang Li Shuaiqiang Wang
Heterogeneous Information Networks (HINs), which consist of various types of
nodes and edges, have recently demonstrated excellent performance in graph
mining. However, most existing heterogeneous graph neural networks (HGNNs)
ignore the problems of missing attributes, inaccurate attributes and scarce
labels for nodes, which limits their expressiveness. In this paper, we propose
a generative self-supervised model SHAVA to address these issues
simultaneously. Specifically, SHAVA first initializes all the nodes in the
graph with a low-dimensional representation matrix. After that, based on the
variational graph autoencoder framework, SHAVA learns both node-level and
attribute-level embeddings in the encoder, which can provide fine-grained
semantic information to construct node attributes. In the decoder, SHAVA
reconstructs both links and attributes. Instead of directly reconstructing raw
features for attributed nodes, SHAVA generates the initial low-dimensional
representation matrix for all the nodes, based on which raw features of
attributed nodes are further reconstructed to leverage accurate attributes. In
this way, SHAVA can not only complete informative features for non-attributed
nodes, but rectify inaccurate ones for attributed nodes. Finally, we conduct
extensive experiments to show the superiority of SHAVA in tackling HINs with
missing and inaccurate attributes.