Attention-aware Multi-hop Trust Inference in Online Social Networks

Social trust relationship prediction targets using attributes to quantify the interrelationships in trust between users. Most of the existing algorithms do not consider the heterogeneity and semantics of information included in online social networks, leading to low adaptability in capturing user preferences. What’s more, they only focus on directly connected nodes, and treat all the information propagation paths equally, leading to the lack of structure context information. Given the incomplete graph structure on online social networks constructed by existing algorithms, they can hardly have good performance in the trust prediction. In order to solve the above-mentioned problems, we propose a novel Attention-aware Multi-hop Trust Inference (AMTI) model which could capture different features on both nodes and paths adaptively based on the complex contexts and take multi-hop neighbors into account. Specifically, in our model, we construct a heterogeneous graph of three types of nodes: User, Interest, and Relationship as well as two different meta-paths: User-Interest-User, and User-Relative-User. Then, we adopt a two-level attention mechanism to obtain the attention value on both the node level and path level. To incorporate the multi-hop neighbors’ information, we develop a 2-hop attention diffusion to aggregate the information from the indirectly connected nodes. The experimental results on real-world datasets have demonstrated that AMTI outperforms the state-of-the-art methods in terms of the accuracy of social trust prediction.