Malleability of complex networks

TL;DR

This paper introduces the concept of network malleability, quantifying the diversity of possible topological unfoldings of evolving complex networks using entropy-based measures, and analyzes how different topological metrics influence this malleability.

Contribution

It proposes a novel entropy-based measure of network malleability and demonstrates its application to various network types and topological measurements.

Findings

Malleability varies significantly with different topological measurements.

Wikipedia network exhibits the highest malleability among tested networks.

Watts-Strogatz model shows the lowest malleability.

Abstract

Most complex networks are not static, but evolve along time. Given a specific configuration of one such changing network, it becomes a particularly interesting issue to quantify the diversity of possible unfoldings of its topology. In this work, we suggest the concept of malleability of a network, which is defined as the exponential of the entropy of the probabilities of each possible unfolding with respect to a given configuration. We calculate the malleability with respect to specific measurements of the involved topologies. More specifically, we identify the possible topologies derivable from a given configuration and calculate some topological measurement of them (e.g. clustering coefficient, shortest path length, assortativity, etc.), leading to respective probabilities being associated to each possible measurement value. Though this approach implies some level of degeneracy in the mapping from topology to measurement space, it still paves the way to inferring the malleability of specific network types with respect to given topological measurements. We report that the malleability, in general, depends on each specific measurement, with the average shortest path length and degree assortativity typically leading to large malleability values. The maximum malleability was observed for the Wikipedia network and the minimum for the Watts-Strogatz model.