Personality-guided Public-Private Domain Disentangled Hypergraph-Former Network for Multimodal Depression Detection

Depression represents a global mental health challenge requiring efficient and reliable automated detection methods. Current Transformer-or Graph Neural Networks (GNNs)based multimodal depression detection methods face significant challenges in modeling individual differences and cross-modal temporal dependencies across diverse behavioral contexts. Therefore, we propose P 3 HF (Personalityguided Public-Private Domain Disentangled Hypergraph-Former Network) with three key innovations: (1) personalityguided representation learning using LLMs to transform discrete individual features into contextual descriptions for personalized encoding; (2) Hypergraph-Former architecture modeling high-order cross-modal temporal relationships; (3) event-level domain disentanglement with contrastive learning for improved generalization across behavioral contexts. Experiments on MPDD-Young dataset show P 3 HF achieves around 10% improvement on accuracy and weighted F1 for binary and ternary depression classification task over existing methods. Extensive ablation studies validate the independent contribution of each architectural component, confirming that personality-guided representation learning and highorder hypergraph reasoning are both essential for generating robust, individual-aware depression-related representations. The code is released at https://github.com/hacilab/P3HF .