Social Relationship Link Inference Based on Graph Convolutional Networks

Abstract

Abstract In the study of social relationship inference, social relationship link inference aims to infer whether there is a social relationship between users. Most previous works applied the unsupervised graph random walk sampling, which had sampling bias and lost much information. In this paper, a Social Relationship Link Inference Based on Graph Convolutional Networks (SLiGCN) is proposed, which learns the spatiotemporal information of check-ins and the impact of related users’ trajectories. It firstly employs the end-to-end supervised learning and applies a recurrent neural network to extract spatiotemporal sequence features from trajectories. Then the graph convolutional network fuses the features of neighboring nodes and employs the fully connected network to infer social relationship links. Finally, it is evaluated with AUC on three real-world datasets. The experiment results show that, compared with baseline models, it not only avoids hand-crafted feature construction that requires much prior knowledge, but also achieves 10% improvement on average.

Keywords

• Social relationship link inference
• Social relationship inference
• Graph convolutional networks
• Deep learning
• Urban computing