Dual mutual robust graph convolutional network for weakly supervised node classification in social networks of internet of people

Social networks are a crucial component of the Internet of People (IoP), which represents cutting-edge 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 network analysis. Fortunately, the graph convolutional network (GCN) and subsequent variants have achieved remarkable performance on semi-supervised node classification (SSNC). However, previous 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 realm of weakly supervised learning. To cope with the aforementioned challenge, we present a novel dual mutual robust GCN named DMRGCN with inspiration from 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 joint loss function which consists of a weighted combination of supervised loss, mutual loss, and robust loss. Finally, we train the network under the pseudo-siamese network paradigm. 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 on classification accuracy. In particular, under the two conditions of labels without noise and with noise, the node classification accuracy obtained by our proposed DMRGCN can be 3.05% and 6.44% higher than that of the vanilla GCN, respectively.