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

arXiv: https://arxiv.org/abs/2511.12460 · HTML: https://mlsanity.com/paper/2511.12460/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 41 references that are in the map.
- First posted on arXiv 16 Nov 2025.

## Builds on (most cited first, 16 of 16)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations](https://mlsanity.com/paper/1811.12359/) (2018)
- [MISA: Modality-Invariant and -Specific Representations for Multimodal Sentiment Analysis](https://mlsanity.com/paper/2005.03545/) (2020)
- [EPIC-Fusion: Audio-Visual Temporal Binding for Egocentric Action Recognition](https://mlsanity.com/paper/1908.08498/) (2019)
- [DialogueGCN: A Graph Convolutional Neural Network for Emotion Recognition in Conversation](https://mlsanity.com/paper/1908.11540/) (2019)
- [Automatic Depression Detection: An Emotional Audio-Textual Corpus and a GRU/BiLSTM-based Model](https://mlsanity.com/paper/2202.08210/) (2022)
- [Utilizing Neural Networks and Linguistic Metadata for Early Detection of Depression Indications in Text Sequences](https://mlsanity.com/paper/1804.07000/) (2018)
- [Robust Unsupervised Domain Adaptation for Neural Networks via Moment Alignment](https://mlsanity.com/paper/1711.06114/) (2017)
- [Beyond Silent Letters: Amplifying LLMs in Emotion Recognition with Vocal Nuances](https://mlsanity.com/paper/2407.21315/) (2024)
- [Self-attention fusion for audiovisual emotion recognition with incomplete data](https://mlsanity.com/paper/2201.11095/) (2022)
- [DepMamba: Progressive Fusion Mamba for Multimodal Depression Detection](https://mlsanity.com/paper/2409.15936/) (2024)
- [The First MPDD Challenge: Multimodal Personality-aware Depression Detection](https://mlsanity.com/paper/2505.10034/) (2025)
- [TaGAT: Topology-Aware Graph Attention Network For Multi-modal Retinal Image Fusion](https://mlsanity.com/paper/2407.14188/) (2024)
- [Non-uniform Speaker Disentanglement For Depression Detection From Raw Speech Signals](https://mlsanity.com/paper/2306.01861/) (2023)
- [Large Language Model Enhanced Machine Learning Estimators for Classification](https://mlsanity.com/paper/2405.05445/) (2024)

## Built on by (most cited first, 2 of 2)

- [FinInvest-GTCN: Explainable Graph-Temporal-Causal Modeling for Risk-Aware Investment Decision Optimization](https://mlsanity.com/paper/2606.28933/) (2026)
- [HiPAD: A Unified Gait, Audio-Visual, and Personality Fusion Network for Depression Detection, with a Hierarchical Personality Ablation and the Calibration Limits of Domain-Adapted LLMs](https://mlsanity.com/paper/2610.14961/) (2026)

## Abstract (arXiv)

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 P3HF (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 P3HF 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.