Multiscale Information Propagation in Emergent Functional Networks.

Arsham Ghavasieh,Manlio De Domenico

doi:10.3390/e23101369

Abstract

Complex biological systems consist of large numbers of interconnected units, characterized by emergent properties such as collective computation. In spite of all the progress in the last decade, we still lack a deep understanding of how these properties arise from the coupling between the structure and dynamics. Here, we introduce the multiscale emergent functional state, which can be represented as a network where links encode the flow exchange between the nodes, calculated using diffusion processes on top of the network. We analyze the emergent functional state to study the distribution of the flow among components of 92 fungal networks, identifying their functional modules at different scales and, more importantly, demonstrating the importance of functional modules for the information content of networks, quantified in terms of network spectral entropy. Our results suggest that the topological complexity of fungal networks guarantees the existence of functional modules at different scales keeping the information entropy, and functional diversity, high.

Highlights

The underlying topology of complex systems is vital for their function, as it regulates the interactions between a system’s units and constrains the pathways for information flow which are necessary for the system to operate
We proposed a novel perspective in terms of emergent functional states, represented as networks where links encode the flow exchange between the nodes, according to diffusion processes
We analyzed classical metrics of the emergent functional networks, such as degree distribution, encoding the total flow exchange of nodes within the networks, and the number and content of functional modules, obtained from feeding the emergent functional state as a network into the Louvain algorithm, which is based on modularity maximization

Summary

Introduction

The underlying topology of complex systems is vital for their function, as it regulates the interactions between a system’s units and constrains the pathways for information flow which are necessary for the system to operate. One can use random walk dynamics to gain insights about how information flow is locally trapped, allowing one, for instance, to uncover the functional mesoscale organization of classical [1], multilayer [2] and higher-order [3,4,5] networks, even across distinct scales [6,7,8,9] Other exploration dynamics, such as walks, can be used to capture the communicability between units and gain insights about the role of each node in exchanging information through the network [10,11,12,13]. One can employ higher-order models of the network structure [14,15] and study specific dynamics on the top of them, from synchronization [16,17,18] to social contagion [19,20,21], for instance

Methods

Results

Conclusion