Non-parametric resampling of random walks for spectral network clustering

TL;DR

This paper introduces a non-parametric resampling method for the transition matrix of random walks to enhance spectral clustering in complex networks, improving community detection robustness.

Contribution

It presents a novel non-parametric resampling approach for the transition matrix, improving spectral clustering performance in network analysis.

Findings

Resampling improves community detection accuracy.

Method tested on synthetic and real networks.

Enhances robustness of spectral algorithms.

Abstract

Parametric resampling schemes have been recently introduced in complex network analysis with the aim of assessing the statistical significance of graph clustering and the robustness of community partitions. We propose here a method to replicate structural features of complex networks based on the non-parametric resampling of the transition matrix associated with an unbiased random walk on the graph. We test this bootstrapping technique on synthetic and real-world modular networks and we show that the ensemble of replicates obtained through resampling can be used to improve the performance of standard spectral algorithms for community detection.