Dynamics and Processing in Finite Self-Similar Networks

TL;DR

This paper investigates how self-similar biological networks' topology influences signal propagation, noise response, and dynamical timescales, revealing complex behaviors dependent on network features like branching and loops.

Contribution

It provides a detailed analysis of the relationship between topology and dynamics in finite self-similar networks, highlighting differences based on structural features.

Findings

Networks with loops facilitate better signal propagation.

High noise networks show greater integration, reversed at low noise.

Small-world networks have slower dynamics and may be less integrated.

Abstract

A common feature of biological networks is the geometric property of self-similarity. Molecular regulatory networks through to circulatory systems, nervous systems, social systems and ecological trophic networks, show self-similar connectivity at multiple scales. We analyze the relationship between topology and signaling in contrasting classes of such topologies. We find that networks differ in their ability to contain or propagate signals between arbitrary nodes in a network depending on whether they possess branching or loop-like features. Networks also differ in how they respond to noise, such that one allows for greater integration at high noise, and this performance is reversed at low noise. Surprisingly, small-world topologies, with diameters logarithmic in system size, have slower dynamical timescales, and may be less integrated (more modular) than networks with longer path lengths. All of these phenomena are essentially mesoscopic, vanishing in the infinite limit but producing strong effects at sizes and timescales relevant to biology.