Long-Range Navigation on Complex Networks using L\'evy Random Walks

TL;DR

This paper presents a Le9vy random walk-based navigation strategy for complex networks, demonstrating its efficiency in reducing coverage time and transforming large-world networks into small-world networks.

Contribution

It introduces a generalized Le9vy walk navigation method with exact analytical results, bridging Le9vy strategies and network dynamics.

Findings

Le9vy walks improve network coverage efficiency

Le9vy navigation transforms large-world networks into small-world networks

Exact expressions for key network navigation metrics are derived

Abstract

We introduce a strategy of navigation in undirected networks, including regular, random, and complex networks, that is inspired by L\'evy random walks, generalizing previous navigation rules. We obtained exact expressions for the stationary probability distribution, the occupation probability, the mean first passage time, and the average time to reach a node on the network. We found that the long-range navigation using the L\'evy random walk strategy, compared with the normal random walk strategy, is more efficient at reducing the time to cover the network. The dynamical effect of using the L\'evy walk strategy is to transform a large-world network into a small world. Our exact results provide a general framework that connects two important fields: L\'evy navigation strategies and dynamics on complex networks.