Trust-Driven Diffusion Models for Online Social Networks

Generative models have achieved significant success in image and audio tasks and have recently been adapted to handle textual data by learning discrete data distributions. However, their application to trust relationship generation remains largely unexplored. This paper addresses this gap by investigating how online social networks structures influence the prediction of trust relationships through structural analysis. We propose DiffTrust++, a novel self-conditioned diffusion model tailored for generating trust relationships in social environments. First, we learn and encode structural equivalence in online social networks and user representations. Second, recognizing that online social networks often follow power-law distributions, we incorporate diffusion degrees to model varying densities under different noise distributions. Finally, asymmetric trust relationships among social entities are generated and evaluated based on their structural features. To evaluate the effectiveness of DiffTrust++, we conducted experiments using real-world datasets and compared our model with benchmark approaches. The experimental results clearly show that our model excels in generating trust relationships, outperforming the alternatives.