Abstract
Understanding and solving the psychological health problems of college students have become a focus of social attention. Complex networks have become important tools to study the factors affecting psychological health, and the Gaussian graphical model is often used to estimate psychological networks. However, previous studies leave some gaps to overcome, including the following aspects. (1) When studying networks of subpopulations, the estimation neglects the intrinsic relationships among subpopulations, leading to a large difference between the estimated network and the real network. (2) Because of the high cost, previous psychological surveys often have a small sample size, and the psychological description is insufficient. Here, the intrinsic connections among multiple tasks are used, and multitask machine learning is applied to develop a multitask Gaussian graphical model. The psychological networks of the population and subpopulations are estimated based on psychological questionnaire data. This study is the first to apply a psychological network to such a large-scale college student psychological analysis, and we obtain some interesting results. The model presented here is a dynamic model based on complex networks which predicts individual behavior and provides insight into the intrinsic links among various symptoms.
Highlights
With the continuous development of science and technology and the continuous progress of society, the pressure on college students in terms of learning, life, emotions, and employment has increased substantially
The intrinsic connections among multiple tasks are used, and multitask machine learning is applied to develop a multitask Gaussian graphical model. e psychological networks of the population and subpopulations are estimated based on psychological questionnaire data. is study is the first to apply a psychological network to such a large-scale college student psychological analysis, and we obtain some interesting results. e model presented here is a dynamic model based on complex networks which predicts individual behavior and provides insight into the intrinsic links among various symptoms
Understanding and solving the psychological health problems of college students have become a focus of social attention
Summary
With the continuous development of science and technology and the continuous progress of society, the pressure on college students in terms of learning, life, emotions, and employment has increased substantially. Psychological research can help to provide targeted psychological counseling to college students, reduce their chances of suffering from psychological diseases, and help improve students’ psychological health. E results of these methods have large errors in the generated network when processing small sample data containing noise. A network estimation method based on a multitask Gaussian graph model is proposed to solve the above problems. Based on a large quantity of college psychological questionnaire data, we estimate and analyze the psychological networks of small subpopulations based on big data samples. (ii) e multitask Gaussian model utilizes the intrinsic links among multiple tasks, thereby reducing the error in estimating networks of small samples with noise and providing a machine learning perspective to study psychological networks.
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