  Trust plays an essential role in an individual's decision-making. Traditional
trust prediction models rely on pairwise correlations to infer potential
relationships between users. However, in the real world, interactions between
users are usually complicated rather than pairwise only. Hypergraphs offer a
flexible approach to modeling these complex high-order correlations (not just
pairwise connections), since hypergraphs can leverage hyperedeges to link more
than two nodes. However, most hypergraph-based methods are generic and cannot
be well applied to the trust prediction task. In this paper, we propose an
Adaptive Hypergraph Network for Trust Prediction (AHNTP), a novel approach that
improves trust prediction accuracy by using higher-order correlations. AHNTP
utilizes Motif-based PageRank to capture high-order social influence
information. In addition, it constructs hypergroups from both node-level and
structure-level attributes to incorporate complex correlation information.
Furthermore, AHNTP leverages adaptive hypergraph Graph Convolutional Network
(GCN) layers and multilayer perceptrons (MLPs) to generate comprehensive user
embeddings, facilitating trust relationship prediction. To enhance model
generalization and robustness, we introduce a novel supervised contrastive
learning loss for optimization. Extensive experiments demonstrate the
superiority of our model over the state-of-the-art approaches in terms of trust
prediction accuracy. The source code of this work can be accessed via
https://github.com/Sherry-XU1995/AHNTP.
