Multi-Relation Aware Heterogeneous Graph Transformer for Robust Spammer Detection via Contrastive Learning in Social Networks

The rapid growth of social networks has significantly increased the prevalence of spammer activities, which poses substantial challenges to user trust, experience, and public safety. Existing spam detection methods primarily rely on easily circumvented handcrafted features and fail to capture the complexities of user behavior and social relationships fully. These methods often struggle to differentiate users in highly heterogeneous networks and do not adequately address the issue of class imbalance. To overcome these limitations, we propose a novel framework, Multi-Relation Aware Heterogeneous Graph Transformer (MRHGT), which effectively integrates heterogeneous graph representation learning to capture complex social structures. Our approach introduces a novel aggregation mechanism that combines relation-aware multi-head attention, relation-specific graph convolution networks, and cross-attention feature fusion. Additionally, we incorporate a heterogeneous graph contrastive learning strategy to handle class imbalance. Extensive experiments on real-world datasets demonstrate that our method outperforms state-of-the-art approaches in accuracy and robustness.