Non-Backtracking Centrality Based Random Walk on Networks

TL;DR

This paper introduces a new biased random walk model called non-backtracking centrality based random walk (NBCRW) that prefers high non-backtracking centrality neighbors, offering advantages over traditional methods in network analysis.

Contribution

The paper develops a theoretical framework for NBCRW, enabling fast computation of key properties and compares its performance with existing biased random walks on various networks.

Findings

NBCRW's stationary distribution differs significantly from TURW and MERW.

NBCRW performs better on heterogeneous networks.

Theoretical methods enable efficient computation of NBCRW properties.

Abstract

Random walks are a fundamental tool for analyzing realistic complex networked systems and implementing randomized algorithms to solve diverse problems such as searching and sampling. For many real applications, their actual effect and convenience depend on the properties (e.g. stationary distribution and hitting time) of random walks, with biased random walks often outperforming traditional unbiased random walks (TURW). In this paper, we present a new class of biased random walks, non-backtracking centrality based random walks (NBCRW) on a network, where the walker prefers to jump to neighbors with high non-backtracking centrality that has some advantages over eigenvector centrality. We study some properties of the non-backtracking matrix of a network, on the basis of which we propose a theoretical framework for fast computation of the transition probabilities, stationary distribution, and hitting times for NBCRW on the network. Within the paradigm, we study NBCRW on some model and real networks and compare the results with those corresponding to TURW and maximal entropy random walks (MERW), with the latter being biased random walks based on eigenvector centrality. We show that the behaviors of stationary distribution and hitting times for NBCRW widely differ from those associated with TURW and MERW, especially for heterogeneous networks.