The relationship between structure and function in locally observed complex networks

TL;DR

This paper introduces a new measure to analyze how local network structure influences node dynamics, revealing significant differentiation in both model and real-world networks, including neuronal and epidemic systems.

Contribution

The paper proposes a novel methodology to quantify node-level dynamical differentiation based on network structure, applied to both synthetic models and real-world networks.

Findings

Geographic and Barabási-Albert models generate networks with distinct dynamical groups.

Neuronal network analysis shows interneurons have high dynamical differentiation.

Epidemic model reveals differences in infection times between high and low degree nodes.

Abstract

Recently, some studies started to unveil the wealthy of interactions that occur between groups of nodes when looking at the small scale of interactions taking place in complex networks. Such findings claim for a new systematic methodology to quantify, at node level, how a dynamics is being influenced (or differentiated) by the structure of the underlying system. Here we define a new measure that, based on dynamical characteristics obtained for a large set of initial conditions, compares the dynamical behavior of the nodes present in the system. Through this measure we find that the geographic and Barab\'asi-Albert models have high capacity for generating networks that exhibit groups of nodes with distinct dynamics compared to the rest of the network. The application of our methodology is illustrated with respect of two real systems. In the first we use the neuronal network of the nematode Caenorhabditis elegans to show that the interneurons of the ventral cord of the nematode presents a very large dynamical differentiation when compared to the rest of the network. The second application concerns the SIS epidemic model on an airport network, where we quantify how different the distribution of infection times of high and low degree nodes can be, when compared to the expected value for the network.