Graph-based Semi-supervised Classification with CRF and RNN

Given a partially labeled graph, the semi-supervised problem of node classification is to infer the unknown labels of the unlabeled nodes. We intend to train graph-based classifiers end-to-end based on graph embedding. From the perspective of classification and feature embedding, we present two novel neural network architectures respectively for semi-supervised node classification. Motivated by pixel-level labeling tasks, we introduce Conditional Random Fields (CRFs) to smooth the classification results of Graph Convolutional Network (GCN). By formulating mean-field approximate inference for CRFs as Recurrent Neural Networks, we develop a deep end-to-end network called GCN-CRF, trained with the usual back-propagation algorithm. Moreover, in order to capture k-step relational information, we present Graph Gated Recurrent Units (Graph-GRU), implementing GRU to graph-structured data as a feed-forward process with k hidden layers. Experiments on three benchmark citation network datasets demonstrate that our two approaches outperform several recently proposed methods.