  Graph-based Semi-Supervised Learning (SSL) aims to transfer the labels of a
handful of labeled data to the remaining massive unlabeled data via a graph. As
one of the most popular graph-based SSL approaches, the recently proposed Graph
Convolutional Networks (GCNs) have gained remarkable progress by combining the
sound expressiveness of neural networks with graph structure. Nevertheless, the
existing graph-based methods do not directly address the core problem of SSL,
i.e., the shortage of supervision, and thus their performances are still very
limited. To accommodate this issue, a novel GCN-based SSL algorithm is
presented in this paper to enrich the supervision signals by utilizing both
data similarities and graph structure. Firstly, by designing a semi-supervised
contrastive loss, improved node representations can be generated via maximizing
the agreement between different views of the same data or the data from the
same class. Therefore, the rich unlabeled data and the scarce yet valuable
labeled data can jointly provide abundant supervision information for learning
discriminative node representations, which helps improve the subsequent
classification result. Secondly, the underlying determinative relationship
between the data features and input graph topology is extracted as
supplementary supervision signals for SSL via using a graph generative loss
related to the input features. Intensive experimental results on a variety of
real-world datasets firmly verify the effectiveness of our algorithm compared
with other state-of-the-art methods.
