Interaction-based HPC modeling of social, biological, and economic contagions over large networks

Modeling large-scale stochastic systems of heterogeneous individuals and their interactions, where multiple behaviors and contagions co-evolve with multiple interaction networks, requires high performance computing and agent-based simulations. We present graph dynamical systems as a formalism to reason about network dynamics and list phenomena from several application domains that have been modeled as graph dynamical systems to demonstrate its wide-ranging applicability. We describe and contrast three tools developed in our laboratory that use this formalism to model these systems. Beyond evaluating system dynamics, we are interested in understanding how to control contagion processes using resources both endogenous and exogenous to the system being investigated to support public policy decision-making. We address control methods, such as interventions, and provide illustrative simulation results.