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

13 Pages Posted: 22 Mar 2018

See all articles by Jonathan Mellon

Jonathan Mellon

University of Manchester

Dan Evans

United States Military Academy, West Point - Network Science Center

Date Written: March 19, 2018

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.

Keywords: Social Network, Disruption, Recovery, Intervention, ERGM, SAOM, SIENA, Logistics, Neuroscience, Key Players

Suggested Citation

Mellon, Jonathan and Evans, Dan, Forecasting Social Network Reaction to Disruption: Current Practices and New Directions (March 19, 2018). Available at SSRN: https://ssrn.com/abstract=3144118 or http://dx.doi.org/10.2139/ssrn.3144118

Jonathan Mellon (Contact Author)

University of Manchester ( email )

Oxford Road
Manchester, M13 9PL
United Kingdom

Dan Evans

United States Military Academy, West Point - Network Science Center ( email )

Thayer Hall Room 119
West Point, NY 10996
United States

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