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 (Personality-guided Public-Private Domain Disentangled Hypergraph-Former Network) with three key innovations: (1) personality-guided 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 high-order hypergraph reasoning are both essential for generating robust, individual-aware depression-related representations. The code is released at https://github.com/hacilab/P3HF.

翻译：抑郁症是全球性的心理健康挑战，需要高效可靠的自动化检测方法。当前基于Transformer或图神经网络（GNNs）的多模态抑郁症检测方法在建模个体差异和跨模态时间依赖性方面面临显著挑战，尤其是在多样化的行为情境中。为此，我们提出了P$^3$HF（人格引导的公共-私有域解耦超图-Transformer网络），该模型包含三个关键创新点：（1）利用大型语言模型（LLMs）进行人格引导的表征学习，将离散的个体特征转化为情境描述，实现个性化编码；（2）采用超图-Transformer架构建模高阶跨模态时间关系；（3）通过事件级域解耦与对比学习，提升模型在不同行为情境下的泛化能力。在MPDD-Young数据集上的实验表明，P$^3$HF在二元和三元抑郁症分类任务中，相较于现有方法，准确率和加权F1分数均提升了约10%。广泛的消融研究验证了各架构组件的独立贡献，证实了人格引导的表征学习与高阶超图推理对于生成鲁棒且个体感知的抑郁症相关表征均至关重要。代码已发布于https://github.com/hacilab/P3HF。