The rapid growth of social networks and online platforms has heightened the importance of trust evaluation in various applications, including e-commerce, social networking, online collaboration, and mobile crowdsourcing. Traditional trust evaluation methods often rely on handcrafted features and simple models, which fail to fully capture the implicit patterns within the complex, heterogeneous structures of social networks. To address this issue, we propose TrustGTN, a novel method based on Heterogeneous Graph Neural Networks (HGNNs). It incorporates a soft selection mechanism that dynamically adjusts the training matrix weights. This enables it to capture the evolving structural and semantic patterns of the graph. The model can automatically learn important trust chains without the need to manually set their lengths. Experimental results show that TrustGTN outperforms existing trust evaluation methods on public datasets, demonstrating its advantages in handling heterogeneous graph data.
Liu et al. (Mon,) studied this question.