Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Multiscale Information Propagation in Emergent Functional Networks

Version 1 : Received: 27 June 2021 / Approved: 28 June 2021 / Online: 28 June 2021 (14:45:57 CEST)

How to cite: Ghavasieh, A.; De Domenico, M. Multiscale Information Propagation in Emergent Functional Networks. Preprints 2021, 2021060668 (doi: 10.20944/preprints202106.0668.v1). Ghavasieh, A.; De Domenico, M. Multiscale Information Propagation in Emergent Functional Networks. Preprints 2021, 2021060668 (doi: 10.20944/preprints202106.0668.v1).

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 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.

Subject Areas

Information dynamics; Multiscale analysis; Networks entropy; Network density matrix; Fungal networks

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