Control and Spread of Contagion in Networks

37 Pages Posted: 23 Apr 2021 Last revised: 27 May 2021

See all articles by John Higgins

John Higgins

University of Kansas - Department of Economics

Tarun Sabarwal

University of Kansas

Date Written: April 21, 2021

Abstract

We study proliferation of an action in a network coordination game that is generalized to include a tractable, model-based measure of virality to make it more realistic. We present new algorithms to compute contagion thresholds and equilibrium depth of contagion and prove their theoretical properties. These algorithms apply to arbitrary connected networks and starting sets, both with and without virality. Our algorithms are easy to implement and help to quantify relationships previously inaccessible due to computational intractability. Using these algorithms, we study the spread of contagion in scale-free networks with 1,000 players using millions of Monte Carlo simulations. Our results highlight channels through which contagion may spread in networks. Small starting sets lead to greater depth of contagion in less connected networks. Virality amplifies the effect of a larger starting set and may make full network contagion inevitable in cases where it would not occur otherwise. It also brings contagion dynamics closer to a type of singularity. Our model and analysis can be used to understand potential consequences of policies designed to control or spread contagion in networks.

Keywords: Network games, coordination games, contagion, algorithmic computation

JEL Classification: C62, C72

Suggested Citation

Higgins, John and Sabarwal, Tarun, Control and Spread of Contagion in Networks (April 21, 2021). Available at SSRN: https://ssrn.com/abstract=3831245 or http://dx.doi.org/10.2139/ssrn.3831245

John Higgins

University of Kansas - Department of Economics ( email )

1300 Sunnyside Drive
Lawrence, KS 66045-7585
United States

Tarun Sabarwal (Contact Author)

University of Kansas ( email )

1415
Lawrence, KS 66045
United States

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