Transition State Theory for Network Dynamics
This framework offers a method to predict discrete structural changes in networks, such as group formation or role turnover, which is significant for researchers studying social dynamics.
This paper introduces a framework combining change paths, dynamic network modeling, and transition state theory to characterize and predict discrete structural changes in networks. It demonstrates that network change, specifically faction realignment in small groups, can be well-predicted ex ante from cross-sectional models under limited assumptions about microdynamics.
Many classic questions of structural theory concern discrete changes, such as the formation or dissolution of groups, role turnover, or faction realignment. Here, we consider a basic framework combining prior work on change paths and recent advances in dynamic network modeling with ideas from transition state theory. This framework facilitates both characterizing the process of structural change and, in some cases, predicting it. Notably, this approach allows approximate prediction of network change from cross-sectional models, under limited assumptions regarding the underlying microdynamics. We apply this framework to a simple model of faction realignment in small groups, showing that the process through which realignment occurs can be well-predicted ex ante for a number of different network micro-processes.