Centrality measures and thermodynamic formalism for complex networks

TL;DR

This paper introduces Entropy Rank and Free Energy Rank, new centrality measures based on the Ruelle-Bowens random walk, which maximize entropy or free energy, offering improved discrimination and sensitivity over PageRank in network analysis.

Contribution

It proposes novel entropy-based centrality measures for complex networks, extending to disconnected graphs, and compares their effectiveness with existing methods.

Findings

Entropy Rank better discriminates node importance than PageRank.

The measures are more sensitive to medium-scale network details.

They show improved robustness to small network modifications.

Abstract

In the study of small and large networks it is customary to perform a simple random walk, where the random walker jumps from one node to one of its neighbours with uniform probability. The properties of this random walk are intimately related to the combinatorial properties of the network. In this paper we propose to use the Ruelle-Bowens random walk instead, whose probability transitions are chosen in order to maximise the entropy rate of the walk on an unweighted graph. If the graph is weighted, then a free energy is optimised instead of entropy rate. Specifically, we introduce a centrality measure for large networks, which is the stationary distribution attained by the the Ruelle-Bowens random walk; we name it Entropy Rank. We introduce a more general version, able to deal with disconnected networks, under the name of Free Energy Rank. We compare the properties of those centrality measures with the classic PageRank and HITS on both toy and real-life examples, in particular their robustness to small modifications of the network. It is observed that our centrality measures have a better discriminating power than PageRank, being able to distinguish clearly pages that PageRank holds for almost equally interesting, and is more sensitive to the medium-scale details of the graph.