Psychologically-Grounded Graph Modeling for Interpretable Depression Detection

ArXi:2604.24126v1 Announce Type: new Automatic depression detection from conversational interactions holds significant promise for scalable screening but remains hindered by severe data scarcity and a lack of clinical interpretability. Existing approaches typically rely on black-box deep learning architectures that struggle to model the subtle, temporal evolution of depressive symptoms or account for participant-specific heterogeneity.