Variational Inference for Training Graph Neural Networks in Low-Data Regime through Joint Structure-Label Estimation

Graph Neural Networks (GNNs) are one of the prominent methods to solve semi-supervised learning on graphs. However, most of the existing GNN models often need sufficient observed data to allow for effective learning and generalization. In real-world scenarios where complete input graph structure and sufficient node labels might not be achieved easily, GNN models would encounter with severe performance degradation. To address this problem, we propose WSGNN, short for weakly-supervised graph neural network. WSGNN is a flexible probabilistic generative framework which harnesses variational inference approach to solve graph semi-supervised learning in a label-structure joint estimation manner. It collaboratively learns task-related new graph structure and node representations through a two-branch network, and targets a composite variational objective derived from underlying data generation distribution concerning the inter-dependence between scarce observed data and massive missing data. Especially, under weakly-supervised low-data regime where labeled nodes and observed edges are both very limited, extensive experimental results on node classification and link prediction over common benchmarks demonstrate the state-of-the-art performance of WSGNN over strong competitors. Concretely, when only 1 label per class and 1% edges are observed on Cora, WSGNN maintains a decent 52.00% classification accuracy, exceeding GCN by 75.6%.