Translation of network metrics to behavior attributes

Human social networks describe the social interactions among individuals and groups. The interactions of a human social network can be simple or complex, and the interactions among nodes can change depending on the social context. The ability to understand and accurately predict human behaviors has been sought for a long time, and recent studies of social networks by using network theory allow novel predictive models for human behaviors. However, current social network analysis approaches do not effectively identify the social interactions that reveal human behaviors, nor do they display the interactions among nodes or consider the effects of time on the social network. Infoscitex (IST) proposes the Reveal and Predict Social Behavior (RPSB) approach, which analyzes human social behavior with regard to social context. Additionally, RPSB uses directed social network graphs to show the direction of social interaction, and therefore hierarchy and relative importance within a network. RPSB also uses time delay and interaction time in considering an interaction’s importance. At the completion of Phase II, we will integrate RPSB into a distributed software/firmware tool that conforms to service-oriented architecture (SOA) standards.