Control and Spread of Contagion in Networks

TL;DR

This paper develops computationally efficient algorithms to analyze and control the spread of contagion in networks, considering global effects and network resilience, with applications to social networks and societal outcomes.

Contribution

It introduces new algorithms for equilibrium analysis in contagion models that incorporate global effects and network complementarities, enabling practical analysis of large networks.

Findings

Algorithms effectively compute contagion equilibria.

Scale-free networks exhibit specific contagion dynamics.

Policy insights for controlling or spreading contagion are derived.

Abstract

We study proliferation of an action in binary action network coordination games that are generalized to include global effects. This captures important aspects of proliferation of a particular action or narrative in online social networks, providing a basis to understand their impact on societal outcomes. Our model naturally captures complementarities among starting sets, network resilience, and global effects, and highlights interdependence in channels through which contagion spreads. We present new, natural, and computationally tractable algorithms to define and compute equilibrium objects that facilitate the general study of contagion in networks and prove their theoretical properties. 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 analysis provides quantitative and qualitative insight into the design of policies to control or spread contagion in networks. The scope of application is enlarged given the many other situations across different fields that may be modeled using this framework.