Forecasting Social Network Reaction to Disruption: Current Practices and New Directions

Abstract

Intervening in networks can lead to complex and unexpected outcomes. This paper introduces reviews current analytical approaches for forecasting how a network might react to an intervention or disruption: reviewing studies from fields of network science including sociology, computer science, neuroscience, and logistics management. We find a wide range of conflicting theories about how networks recover from disruption but little empirical research on how networks react to disruption, and none at all on how social networks react to disruptions. We suggest several approaches to empirically studying network reactions and using this information to forecast network reactions including the use of Exponential Random Graph Models and Stochastic Actor Oriented Models.