# User Trust Inference in Online Social Networks: A Message Passing Perspective
## 2 Related Work  
The problem of trust inference or trust prediction between two users in online social networks or two nodes in general networks bears some similarity with the link prediction problem, and thus it can be modeled as a link prediction task. However, using a link prediction method to predict trust links requires the method to work with directional links or even signed links, which complicates the link prediction method itself.  
For example, [PERSON] [25] leverages micro-structural patterns and the resulting node similarity to retrieve the probability of a missing directional link existing between two users in a social network. The method's drawback is that the micro-structural pattern is limited to a triad, and consequently it may fail in finding links between any two arbitrary nodes that do not have a common neighbor.  
The work by [PERSON] et al. [26] also employed link prediction techniques to infer friend or trust relationships. In this work, they proposed a generative model, one of the stochastic topic models, to generate social links for users with the consideration of user's interests in \"topical\" and \"social\" resources, e.g., whether a targeted user is an authority on a topic or he/she is recognized by an acquaintance in the real world.  
Trust inference models based on the trust propagation theory are often called \"walk-based\" methods. In [27], [PERSON] et al. developed a trust inference framework which obtains the trust rate between any pair of users by aggregating a set of strong trust paths generated with the knowledge of their weighted similarity about commonly interesting topics and their trust propagation ability in the social network. If the trust rate is above a user-defined threshold, the framework determines that there should be a trust link between the users.  
The work by [PERSON] et al. [28] included a unified model combining both explicit and implicit trust, and infers trust links by using different trust propagation strategies. The three primary trust propagation strategies include direct propagation, transposed trust, and global trust propagation. Other complex strategies, such as co-citations and trust coupling, are combinations of the primary propagation strategies. Other walk-based methods include ModelTrust [29], TidalTrust [30], AssessTrust [31], OpinionWalk [32], etc.  
Trust prediction can also be achieved and improved by using collaborative filtering techniques, particularly the matrix factorization (MF)-based methods. It is also quite convenient for MF-based methods to integrate other types of data that carry trust information. hTrust [33] incorporates low-rank matrix factorization and homophily regularization to infer trust links. The homophily regularization controls how user rating similarity affects predicting user trust relationships. MATRI [34] extracts user trust tendency using matrix factorization from the user trust rating matrix and incorporates trust propagation with trust tendency into a collective matrix factorization framework to infer trust. Another MF-based trust inference model [35] takes advantage of not only matrix-factorized trust tendency and propagated trust, but also similarities of users' rating habits, and achieves good performance.  
Neural networks are also employed for trust evaluation. NeuralWalk [36] employs a neural network named WalkNet to model single-hop trust propagation, and then iteratively assesses unknown multi-hop trust relationships. Although NeuralWalk can achieve good accuracy in trust prediction, it is inefficient due to the massive matrix operations involved in training and test set selection. Besides, the interpretability for such methods based on neural networks is an obvious drawback.