ABSTRACT
Network structure provides critical information for understanding the dynamic behavior of complex systems. However, the complete structure of real-world networks is often unavailable, thus it is crucially important to develop approaches to infer a more complete structure of networks. In this paper, we integrate the configuration model for generating random networks into an Expectation-Maximization-Aggregation (EMA) framework to reconstruct the complete structure of multiplex networks. We validate the proposed EMA framework against the Expectation-Maximization (EM) framework and random model on several real-world multiplex networks, including both covert and overt ones. It is found that the EMA framework generally achieves the best predictive accuracy compared to the EM framework and the random model. As the number of layers increases, the performance improvement of EMA over EM decreases. The inferred multiplex networks can be leveraged to inform the decision-making on monitoring covert networks as well as allocating limited resources for collecting additional information to improve reconstruction accuracy. For law enforcement agencies, the inferred complete network structure can be used to develop more effective strategies for covert network interdiction.
ABSTRACT
Recent research has identified a variety of significant predictors of academic dishonesty, but virtually no studies have examined the conditional relation of binge drinking and students' cheating. Using a survey sample of 289 college students, this study tested the mediating relations of binge drinking with the correlation of independent variables--including opportunity, strain, and self-control--on self-reported academic dishonesty. Low self-control had a stronger correlation with students' cheating behaviors for those who were heavy binge drinkers (beta = -.52) than for students who were nonbingers (beta = -.38). Differential relations were found for other key variables, namely, opportunity and strain, at varying severities of binge drinking.