Transition State Theory for Network Dynamics

TL;DR

This paper introduces a framework combining transition state theory with dynamic network modeling to characterize and predict structural changes in networks, such as faction realignment, from cross-sectional data.

Contribution

It presents a novel approach that enables approximate prediction of network change processes using limited assumptions and cross-sectional models.

Findings

The framework can predict faction realignment processes in small groups.

It allows characterization of structural change pathways.

The approach works under limited assumptions about microdynamics.

Abstract

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.