Change in Connectivity in a Social Network over Time: A Bayesian Perspective

In this paper, we propose a Bayesian methodology for examining differences between statistics of a social network at two distinct points in time. The problem has been of interest for some time in the social networks community because it is quite difficult to test whether differences over time in statistics such as overall network connectivities are significant. Several issues make this problem challenging: links in a social network tend to be dependent, and the networks at the two different points in time are likely to be dependent as well. This implies, for example, that bootstrapping a social network to address this problem may be impractical. This paper expands on a previously published Bayesian version of the model for social networks with random effects, which allows for dependence between the edges of the networks. We use the software Winbugs to obtain posterior distributions for the difference in connectivity over time and for the correlation between each actor’s connectivities in the network at both points in time. We assume that this correlation is the same for all actors. We illustrate our methods with the case of a social network of collaborations (joint publications) between departments of a business university where interdisciplinary work was actively promoted. Our methods allow us to compare the tendency to make collaborative links across departments before and after the administrative initiatives. 1 p