Multifractal Characterization of Protein Contact Networks

TL;DR

This study applies multifractal analysis to protein contact networks by transforming network properties into time series, revealing long-range correlations and insights into network topology through multifractal spectra.

Contribution

It introduces a novel approach of analyzing protein contact networks using multifractal analysis of derived time series, highlighting differences from well-known network models.

Findings

Protein contact networks exhibit long-range correlations.

Time series show behavior between monofractal and multifractal.

Multifractal spectra reveal the role of fluctuations in network topology.

Abstract

The multifractal detrended fluctuation analysis of time series is able to reveal the presence of long-range correlations and, at the same time, to characterize the self-similarity of the series. The rich information derivable from the characteristic exponents and the multifractal spectrum can be further analyzed to discover important insights about the underlying dynamical process. In this paper, we employ multifractal analysis techniques in the study of protein contact networks. To this end, initially a network is mapped to three different time series, each of which is generated by a stationary unbiased random walk. To capture the peculiarities of the networks at different levels, we accordingly consider three observables at each vertex: the degree, the clustering coefficient, and the closeness centrality. To compare the results with suitable references, we consider also instances of three well-known network models and two typical time series with pure monofractal and multifractal properties. The first result of notable interest is that time series associated to proteins contact networks exhibit long-range correlations (strong persistence), which are consistent with signals in-between the typical monofractal and multifractal behavior. Successively, a suitable embedding of the multifractal spectra allows to focus on ensemble properties, which in turn gives us the possibility to make further observations regarding the considered networks. In particular, we highlight the different role that small and large fluctuations of the considered observables play in the characterization of the network topology.