Specific Communication Network Measure Distribution Estimation

: A new method is proposed to estimate the probability distribution of specific communication network measures. Real world communication networks are dynamic and vary based on an underlying social network, thus reliably estimating network measures is challenging. Two individuals that are socially connected may communicate several times one day, and not at all on another, yet their basic relationship remains unchanged. In this situation, estimates of network measures, such as density, degree centrality and others may be severely affected by the occurrence or absence of observed communication ties between individuals. The communication network of a group of mid-career Army officers is modeled from empirical data using the network probability matrix (NPM) proposed by McCulloh and Lospinoso (2007). The NPM provides a framework to model a communication network by estimating the edge probabilities between two individuals in a network. This framework can model a specific social group regardless of their network topology: random, small-world, scale-free, cellular, etc. Monte Carlo simulation is used with the NPM to generate 100,000 instances of the communication network. A statistical distribution is fit to the density measure. Using this probability distribution, statistically significant changes in density can be detected.