Maximal entropy random walk in community finding

TL;DR

This paper explores using the maximal-entropy random walk in community detection algorithms for complex networks, comparing its performance to traditional methods on benchmark graphs.

Contribution

It introduces the maximal-entropy random walk as a new approach for community detection and analyzes its impact on algorithm performance.

Findings

Performance varies significantly depending on the algorithm used.

Maximal-entropy random walk can slightly improve or worsen results.

The approach offers a new perspective on random walk-based community detection.

Abstract

The aim of this paper is to check feasibility of using the maximal-entropy random walk in algorithms finding communities in complex networks. A number of such algorithms exploit an ordinary or a biased random walk for this purpose. Their key part is a (dis)similarity matrix, according to which nodes are grouped. This study encompasses the use of the stochastic matrix of a random walk, its mean first-passage time matrix, and a matrix of weighted paths count. We briefly indicate the connection between those quantities and propose substituting the maximal-entropy random walk for the previously chosen models. This unique random walk maximises the entropy of ensembles of paths of given length and endpoints, which results in equiprobability of those paths. We compare performance of the selected algorithms on LFR benchmark graphs. The results show that the change in performance depends very strongly on the particular algorithm, and can lead to slight improvements as well as significant deterioration.