Dual Mutual Robust Graph Convolutional Network for Weakly Supervised Node Classification in Social Networks of Internet of People

Abstract

Social networks are a crucial component of the Internet of People (IoP) which is the frontier of the next generation of Internet of Things (IoT). Predicting a large number of unknown node labels with few known labels is one of the challenging problems in social networks analysis. Fortunately, Graph Convolution Network (GCN) and subsequent variants have achieved impressive performance on Semi-Supervised Node Classification (SSNC). However, existing works only focus on the case of clean labels and rarely study the problem of SSNC under Noisy Labels (SSNCNL), which is a more challenging and practical problem in the field of weakly supervised learning. To address this challenge, in this paper, we propose a novel Dual Mutual Robust Graph Convolutional Network named DMRGCN, which is inspired by deep mutual learning and robust learning in the domain of image recognition. Specifically, we first employ two GCNs with different learning abilities to construct network architecture. Then we define a new loss function which is a weighted sum of the supervised loss, mutual loss, and robust loss. Finally, we train the network in the paradigm of pseudo-siamese network. Experimental results on three social network benchmark datasets with different levels of noise on labels demonstrate that DMRGCN outperforms the vanilla GCN and several variants in terms of classification accuracy. In particular, under the two conditions of clean labels and noisy labels, the accuracy of node classification obtained by our proposed DMRGCN can be 3.05% and 6.44% higher than that of the vanilla GCN, respectively.