Label Guided Graph Optimized Convolutional Network for Semi-Supervised Learning

Ziyan Zhang, Bo Jiang, Jin Tang, Bin Luo

Abstract: Graph Convolutional Networks (GCNs) have been widely studied for semi-supervised learning tasks. It is known that the graph convolution operations in most of existing GCNs are composed of two parts, i.e., feature propagation (FP) on a neighborhood graph and feature transformation (FT) with a fully connected network. For semi-supervised learning, existing GCNs generally utilize the label information only to train the parameters of the FT part via optimizing the loss function. However, they lack exploiting the label information in neighborhood feature propagation. Besides, due to the fixed graph topology used in FP, existing GCNs are vulnerable w.r.t. structural noises/attacks. To address these issues, we propose a novel and robust Label Guided Graph Optimized Convolutional Network (LabelGOCN) model which aims to fully exploit the label information in feature propagation of GCN via pairwise constraints propagation. In LabelGOCN, the pairwise constraints can provide a kind of ‘weakly’ supervised information to refine graph topology structure and thus to guide graph convolution operations for robust semi-supervised learning tasks. In particular, LabelGOCN jointly refines the pairwise constraints and GCN via a unified regularization model which can boost their respective performance. The experiments on several benchmark datasets show the effectiveness and robustness of the proposed LabelGOCN on semi-supervised learning tasks.