青少年网络适应的拓扑结构分析：基于纵向追踪数据

s the saying goes, Survival of the fittest. Nowadays, the Internet has become a critical channel for information acquisition, social interaction, and educational learning. Adolescents internet adaptation capabilities must be continuously improved to adapt to this rapidly developing information age. Internet adaptation is inherently a multidimensional system encompassing various stages and dimensions. However, there remains a gap in the research exploring the internal topological characteristics and functional mechanisms of internet adaptation. Consequently, this study aims to employ network analysis techniques to elucidate the core characteristics, internal structure, dynamic evolution, and relationships with external variables of adolescents internet adaptation through network analysis. This approach will offer a comprehensive framework for understanding adolescents successful adaptation in the digital age and provide scientific insights for preventing and intervening in adolescent internet addiction. This study collected all data through paper-and-pencil questionnaires. At Time 1, valid data were obtained from 5783 participants (Males for 37.4%, Mage = 17.20 years, SD = 2.62). Five months later, data from 1235 of these participants were tracked (Males for 38%, Mage = 14.98 years, SD = 1.66). Based on the research objectives, we conducted cross-sectional network analysis, network comparison, and cross-lagged network analysis. All cross-sectional and cross-lagged network analyses were primarily conducted using R (V.4.3.2). Network visualizations were created with the qgraph package (version 1.9.5). The accuracy of edge estimates was assessed by performing 1000 bootstrap iterations to construct 95% non-parametric bootstrap confidence intervals for each edge. In the cross-sectional network of internet adaptation, internet curiosity is the node with the highest strength (1.18). Network comparison results indicate no significant difference in the overall strength between the T1 (3.52) and the T2 network (3.79) (p = 0.120), although the network invariance test result is significant (p < 0.001). The cross-lagged network analysis shows that internet self-efficacy has the strongest out-expected influence (0.60), and internet information searching has the strongest in-expected influence (0.30). Additionally, the cross-lagged network analysis of internet adaptation and internet addiction reveals that internet information protection capability exhibits the strongest outgoing predictive ability. The main conclusions are as follows: (1) Adolescents internet curiosity plays a multifaceted role in their internet adaptation process: insufficient curiosity can lead to low internet self-efficacy, while excessive curiosity can result in poor internet self-control; (2) Internet self-efficacy has the most significant impact on the overall development of internet adaptation, serving as the primary driving force. (3) Internet information searching ability is the most internally influenced aspect during the internet adaptation process and is a crucial component of adolescents internet adaptability. (4) Internet information protection capability shows the strongest outgoing predictive ability in the cross-cluster network analysis of internet addiction, warranting further attention in future research.