  In recent years, graph neural networks (GNN) have achieved significant
developments in a variety of graph analytical tasks. Nevertheless, GNN's
superior performance will suffer from serious damage when the collected node
features or structure relationships are partially missing owning to numerous
unpredictable factors. Recently emerged graph completion learning (GCL) has
received increasing attention, which aims to reconstruct the missing node
features or structure relationships under the guidance of a specifically
supervised task. Although these proposed GCL methods have made great success,
they still exist the following problems: the reliance on labels, the bias of
the reconstructed node features and structure relationships. Besides, the
generalization ability of the existing GCL still faces a huge challenge when
both collected node features and structure relationships are partially missing
at the same time. To solve the above issues, we propose a more general GCL
framework with the aid of self-supervised learning for improving the task
performance of the existing GNN variants on graphs with features and structure
missing, termed unsupervised GCL (UGCL). Specifically, to avoid the mismatch
between missing node features and structure during the message-passing process
of GNN, we separate the feature reconstruction and structure reconstruction and
design its personalized model in turn. Then, a dual contrastive loss on the
structure level and feature level is introduced to maximize the mutual
information of node representations from feature reconstructing and structure
reconstructing paths for providing more supervision signals. Finally, the
reconstructed node features and structure can be applied to the downstream node
classification task. Extensive experiments on eight datasets, three GNN
variants and five missing rates demonstrate the effectiveness of our proposed
method.
