{"title":"Heterogeneous Graph Attention Networks for Depression Identification by Campus Cyber-Activity Patterns","authors":"Minqiang Yang;Zhuoheng Li;Yujie Gao;Chen He;Fuzhan Huang;Wenbo Chen","doi":"10.1109/TCSS.2023.3343689","DOIUrl":null,"url":null,"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.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":null,"pages":null},"PeriodicalIF":4.5000,"publicationDate":"2024-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Computational Social Systems","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10398260/","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, CYBERNETICS","Score":null,"Total":0}
引用次数: 0
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.
期刊介绍:
IEEE Transactions on Computational Social Systems focuses on such topics as modeling, simulation, analysis and understanding of social systems from the quantitative and/or computational perspective. "Systems" include man-man, man-machine and machine-machine organizations and adversarial situations as well as social media structures and their dynamics. More specifically, the proposed transactions publishes articles on modeling the dynamics of social systems, methodologies for incorporating and representing socio-cultural and behavioral aspects in computational modeling, analysis of social system behavior and structure, and paradigms for social systems modeling and simulation. The journal also features articles on social network dynamics, social intelligence and cognition, social systems design and architectures, socio-cultural modeling and representation, and computational behavior modeling, and their applications.