Perturbation centrality and Turbine: a novel centrality measure obtained using a versatile network dynamics tool

TL;DR

This paper introduces Turbine, a versatile network simulation framework, and perturbation centrality, a new measure for identifying influential nodes in large networks, validated across biological and social systems.

Contribution

The paper presents Turbine, a general framework for simulating network dynamics, and introduces perturbation centrality, a novel measure for analyzing node influence in complex networks.

Findings

High perturbation centrality nodes are key in intra-protein communication.

Perturbation centrality changes reflect functional shifts in stressed yeast cells.

Turbine and perturbation centrality are validated across diverse network types.

Abstract

Analysis of network dynamics became a focal point to understand and predict changes of complex systems. Here we introduce Turbine, a generic framework enabling fast simulation of any algorithmically definable dynamics on very large networks. Using a perturbation transmission model inspired by communicating vessels, we define a novel centrality measure: perturbation centrality. Hubs and inter-modular nodes proved to be highly efficient in perturbation propagation. High perturbation centrality nodes of the Met-tRNA synthetase protein structure network were identified as amino acids involved in intra-protein communication by earlier studies. Changes in perturbation centralities of yeast interactome nodes upon various stresses well recapitulated the functional changes of stressed yeast cells. The novelty and usefulness of perturbation centrality was validated in several other model, biological and social networks. The Turbine software and the perturbation centrality measure may provide a large variety of novel options to assess signaling, drug action, environmental and social interventions. The Turbine algorithm is available at: http://www.turbine.linkgroup.hu