Inferring Networks of Interdependent Labor Skills to Illuminate Urban Economic Structure

Labor-Demand-Side Economic Development Incentives and Urban Opportunity

Targeted Damage to Interdependent Networks

The giant mutually connected component (GMCC) of an interdependent or multiplex network collapses with a discontinuous hybrid transition under random damage to the network. If the nodes to be damaged are selected in a targeted way, the collapse of the GMCC may occur significantly sooner. Finding the minimal damage set which destroys the largest mutually connected component of a given interdepen...

Efficiently inferring community structure in bipartite networks

Bipartite networks are a common type of network data in which there are two types of vertices, and only vertices of different types can be connected. While bipartite networks exhibit community structure like their unipartite counterparts, existing approaches to bipartite community detection have drawbacks, including implicit parameter choices, loss of information through one-mode projections, a...

Recovery of Interdependent Networks.

Recent network research has focused on the cascading failures in a system of interdependent networks and the necessary preconditions for system collapse. An important question that has not been addressed is how to repair a failing system before it suffers total breakdown. Here we introduce a recovery strategy for nodes and develop an analytic and numerical framework for studying the concurrent ...

Inoperability input-output modeling of disruptions to interdependent economic systems

In this study, the Inoperability Input-Output Model (IIM) is deployed for assessing the impacts of disruptive events on interconnected economic systems. The IIM is based on Wassily Leontief’s input-output model which is capable of describing the ripple effects of disruptions to interdependent systems. Besides describing economic impact in financial terms, the “inoperability” metric is also used...