The effect of temporal aggregation level in social network monitoring

• Published in: PloS one 2018-12, Vol.13 (12), p.e0209075
• Main Authors: Zhao, Meng J, Driscoll, Anne R, Sengupta, Srijan, Stevens, Nathaniel T, Fricker, Jr, Ronal D, Woodall, William H
• Format: Article
• Language: English
• Subjects: Agglomeration; Analysis; Anomalies; Biology and Life Sciences; Communication; Computer and Information Sciences; Computer Simulation; Ecology and Environmental Sciences; Humans; Interpersonal relations; Methods; Modern society; Monitoring; Organization theory; Research and Analysis Methods; Signal Processing, Computer-Assisted; Social factors; Social interaction; Social interactions; Social Networking; Social networks; Social organization; Social Sciences; Sociology; Statistical analysis; Stochastic Processes; Stochasticity; Surveillance; Time; Time Factors
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0209075

Social networks have become ubiquitous in modern society, which makes social network monitoring a research area of significant practical importance. Social network data consist of social interactions between pairs of individuals that are temporally aggregated over a certain interval of time, and the level of such temporal aggregation can have substantial impact on social network monitoring. There have been several studies on the effect of temporal aggregation in the process monitoring literature, but no studies on the effect of temporal aggregation in social network monitoring. We use the degree corrected stochastic block model (DCSBM) to simulate social networks and network anomalies and analyze these networks in the context of both count and binary network data. In conjunction with this model, we use the Priebe scan method as the monitoring method. We demonstrate that temporal aggregation at high levels leads to a considerable decrease in the ability to detect an anomaly within a specified time period. Moreover, converting social network communication data from counts to binary indicators can result in a significant loss of information, hindering detection performance. Aggregation at an appropriate level with count data, however, can amplify the anomalous signal generated by network anomalies and improve detection performance. Our results provide both insights on the practical effects of temporal aggregation and a framework for the study of other combinations of network models, surveillance methods, and types of anomalies.