Search in weighted complex networks

TL;DR

This paper investigates local search algorithms in weighted complex networks, demonstrating that local betweenness centrality (LBC) effectively exploits heterogeneity in node degrees and edge weights to optimize search performance across various network types.

Contribution

It introduces the use of local betweenness centrality (LBC) as a universal, efficient search strategy in heterogeneous weighted complex networks.

Findings

LBC-based search outperforms other methods in scale-free weighted networks.

Heterogeneity in node degree and edge weights enhances search efficiency.

LBC performs well across a broad class of complex networks.

Abstract

We study trade-offs presented by local search algorithms in complex networks which are heterogeneous in edge weights and node degree. We show that search based on a network measure, local betweenness centrality (LBC), utilizes the heterogeneity of both node degrees and edge weights to perform the best in scale-free weighted networks. The search based on LBC is universal and performs well in a large class of complex networks.