{"ID":2840911,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.12460","arxiv_id":"2511.12460","title":"Personality-guided Public-Private Domain Disentangled Hypergraph-Former Network for Multimodal Depression Detection","abstract":"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.","short_abstract":"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 acr...","url_abs":"https://arxiv.org/abs/2511.12460","url_pdf":"https://arxiv.org/pdf/2511.12460v1","authors":"[\"Changzeng Fu\",\"Shiwen Zhao\",\"Yunze Zhang\",\"Zhongquan Jian\",\"Shiqi Zhao\",\"Chaoran Liu\"]","published":"2025-11-16T05:14:37Z","proceeding":"cs.LG","tasks":"[\"cs.LG\",\"cs.AI\"]","methods":"[\"Graph Neural Network\",\"Transformer\",\"Large Language Model\"]","has_code":false,"code_links":[{"ID":607011,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2840911,"paper_url":"https://arxiv.org/abs/2511.12460","paper_title":"Personality-guided Public-Private Domain Disentangled Hypergraph-Former Network for Multimodal Depression Detection","repo_url":"https://github.com/hacilab/P3HF","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}