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.

通过引入生成自监督模型SHAVA，本文提出了一种可以同时解决节点属性缺失、属性不准确和标签稀缺问题的方法，该方法在异构信息网络中构建了低维表示矩阵，并通过节点和属性的编码解码实现了节点属性的精细语义信息表达、属性重建和准确性纠正。实验结果表明，SHAVA在处理具有缺失和不准确属性的异构信息网络方面具有优越性。

自主学习的异构图变分自编码器