“Centrality in Stochastic Networks”

Teaser: In many economic and managerial applications, there is an underlying network of connections that governs a dynamical system. In these applications, it is not only the underlying network that shapes outcomes, but also how external agents target or influence the network (e.g., how disinformation spreads from targeted social media users). We develop an appropriate centrality measure (“targeting centrality”) that captures a wide array of settings and characterize its distribution in networks under uncertainty.

Abstract: Centrality measures are ubiquitous, appearing in models of opinion formation, macroeconomics, and consumption with externalities. With few exceptions, most of the previous literature has focused on modeling centrality in settings where the underlying network structure is known and remains static. This paper expands on this work by considering arbitrary row-stochastic random networks that may be evolving over time. Under mild assumptions, we show that all centrality measures are, with high probability, close to their values in an appropriately-defined “average” network. We conclude by demonstrating how this result offers a major technical simplification for the dynamic and stochastic analyses of several applications.