Guardian: Evaluating Trust in Online Social Networks with Graph Convolutional Networks

In modern online social networks, each user is typically able to provide a value to indicate how trustworthy their direct friends are. Inferring such a value of social trust between any pair of nodes in online social networks is useful in a wide variety of applications, such as online marketing and recommendation systems. However, it is challenging to accurately and efficiently evaluate social trust between a pair of users in online social networks. Existing works either designed handcrafted rules that rely on specialized domain knowledge, or required a significant amount of computation resources, which affected their scalability.

In recent years, graph convolutional neural networks (GCNs) have been shown to be powerful in learning on graph data. Their advantages provide great potential to trust evaluation as social trust can be represented as graph data. In this paper, we propose Guardian 1 , a new end-to-end framework that learns latent factors in social trust with GCNs. Guardian is designed to incorporate social network structures and trust relationships to estimate social trust between any two users. Extensive experimental results demonstrated that Guardian can speedup trust evaluation by up to 2, 827× with comparable accuracy, as compared to the stateof-the-art in the literature.