Visual Analytics of Simulation Ensembles for Network Dynamics

Abstract

A central question in the field of Network Science is to analyze the role of a given network topology on the dynamical behavior captured by time-varying simulations executed on the network. These dynamical systems are also influenced by global simulation parameters. We present a visual analytics approach that supports the investigation of the impact of the parameter settings, i.e., how parameter choices change the role of network topology on the simulations' dynamics. To answer this question, we are analyzing ensembles of simulation runs with different parameter settings executed on a given network topology. We relate the nodes' topological structures to their dynamical similarity in a 2D plot based on an interactively defined hierarchy of topological properties and a 1D embedding for the dynamical similarity. We evaluate interactively defined topological groups with respect to matching dynamical behavior, which we visually encode as graphs of the function of the considered simulation parameter. Interactive filtering and coordinated views allow for a detailed analysis of the parameter space with respect to topology-dynamics relations. Our visual analytics approach is applied to scenarios for excitable dynamics on synthetic and real brain connectome networks.