DualGraph: Improving Semi-supervised Graph Classification via Dual Contrastive Learning

In this paper, we study semi-supervised graph classification, a fundamental problem in data mining and machine learning. The problem is typically solved by learning graph neural networks with pseudo-labeling or knowledge distillation to incorporate both labeled and unlabeled graphs. However, these methods usually either suffer from overconfident and biased pseudo-labels or suboptimal distillation caused by the insufficient use of unlabeled data. Inspired by the recent progress of contrastive learning and dual learning, we propose DualGraph, a principled framework to leverage unlabeled graphs more effectively for semi-supervised graph classification. DualGraph consists of a prediction module and a retrieval module to model graphs
$G$
and their labels
$y$
from opposite while complementary views (i.e., p(y | G) and p(G | y) respectively). The two modules are jointly trained via posterior regularization, which encourages their inter-module consistency on unlabeled graphs. Moreover, we improve model training for each module with a contrastive learning framework to encourage the intra-module consistency on unlabeled data. Experimental results on a range of publicly accessible datasets reveal the effectiveness of our DualGraph.