Estimating the Shape of Covert Networks

Social network analysis has been used to understand groups of individuals and how they operate. Most of the literature in social networks has dealt with overt organizations with an easily discernable network structure. This paper examines the possibilities of using the inherent structures observed in social networks to make predictions of networks using limited and missing information. The model is based on empirical network data exhibiting the structural properties of triad closure and adjacency. Triad closure indicates that if person i has a dyad with person j and person j has a dyad with person k, then there is a higher than chance likelihood that person i and person k have a dyad. Adjacency is a corollary of triad closure stating that if person i has a dyad with person j, it is more than likely that person i has a dyad with person k. The model exploits these properties using an inference model to update adjacent dyads given information on a reference dyad. The model is tested against several networks to understand and discern its behavior. The paper illustrates that if the model is built with careful consideration towards the network being predicted, it may assist in making better decisions regarding uncertain organizational phenomenon. However, the model performs relatively poorly if there is a disproportionate amount of information either supporting or not supporting a dyad and/or if dyadic priors are well informed. The method is applied in a covert network example, and has been extended for epidemiological networks and improving performance in organizations operating under stress. The paper opens up new avenues in the development of models