  Massive social media data can reflect people's authentic thoughts, emotions,
communication, etc., and therefore can be analyzed for early detection of
mental health problems such as depression. Existing works about early
depression detection on social media lacked interpretability and neglected the
heterogeneity of social media data. Furthermore, they overlooked the global
interaction among users. To address these issues, we develop a novel method
that leverages a Heterogeneous Subgraph Network with Prompt Learning(HSNPL) and
contrastive learning mechanisms. Specifically, prompt learning is employed to
map users' implicit psychological symbols with excellent interpretability while
deep semantic and diverse behavioral features are incorporated by a
heterogeneous information network. Then, the heterogeneous graph network with a
dual attention mechanism is constructed to model the relationships among
heterogeneous social information at the feature level. Furthermore, the
heterogeneous subgraph network integrating subgraph attention and
self-supervised contrastive learning is developed to explore complicated
interactions among users and groups at the user level. Extensive experimental
results demonstrate that our proposed method significantly outperforms
state-of-the-art methods for depression detection on social media.
