Searchability of central nodes in networks

TL;DR

This paper investigates how effectively the most central nodes in social networks can be identified through local search methods, highlighting the superior searchability of eigenvector centrality, especially in epidemic spreading scenarios.

Contribution

It demonstrates that eigenvector centrality enables near-optimal local searchability in networks, outperforming degree and betweenness measures, with implications for epidemic control.

Findings

Eigenvector centrality shows high searchability in networks.

Searchability is greater in supercritical epidemic spreading.

Eigenvector-based search outperforms degree and betweenness searches.

Abstract

Social networks are discrete systems with a large amount of heterogeneity among nodes (individuals). Measures of centrality aim at a quantification of nodes' importance for structure and function. Here we ask to which extent the most central nodes can be found by purely local search. We find that many networks have close-to-optimal searchability under eigenvector centrality, outperforming searches for degree and betweenness. Searchability of the strongest spreaders in epidemic dynamics tends to be substantially larger for supercritical than for subcritical spreading.