Heterogeneous Graph Attention Networks for Depression Identification by Campus Cyber-Activity Patterns

Abstract

As one of the most prevalent mental disorders, depression is associated with a high rate of self-harm and suicide, particularly among college students. It is urgently needed to discover prospective cases of depression disorder among college students, enabling timely intervention to reduce its impact on their academic performance and daily lives. This study investigates a method for identifying groups that may have early depressive tendencies through their Internet usage on campus networks. This article proposes a heterogeneous graph attention network (H-GAT) model that incorporates an attention mechanism based on ablation experiments in heterogeneous graphs to analyze the patterns and correlations within the surfing behavior data of students. This model makes full use of the interaction relationships between heterogeneous nodes in the graph to capture the affective tendencies reflected in the cyber-activity patterns. The proposed H-GAT model exhibits excellent performance, with nearly 80% accuracy and recall. Our work offers a potential approach to detect depression on college campuses using nonintrusive methods, which could ultimately contribute to early warnings for both individuals experiencing depression and higher education institutions.