Searchability of Central Nodes in Networks

Konstantin Klemm

Research output: Contribution to journalArticle

2 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)707-719
Number of pages13
JournalJournal of Statistical Physics
Volume151
Issue number3-4
DOIs
Publication statusPublished - 2013
Externally publishedYes

Fingerprint

Centrality
Vertex of a graph
eigenvectors
Betweenness
Discrete Systems
Local Search
Quantification
Social Networks
Eigenvector
Tend

Keywords

  • Frustration
  • Markov chain
  • Network centrality
  • Social network

ASJC Scopus subject areas

  • Statistical and Nonlinear Physics
  • Mathematical Physics

Cite this

Searchability of Central Nodes in Networks. / Klemm, Konstantin.

In: Journal of Statistical Physics, Vol. 151, No. 3-4, 2013, p. 707-719.

Research output: Contribution to journalArticle

Klemm, Konstantin. / Searchability of Central Nodes in Networks. In: Journal of Statistical Physics. 2013 ; Vol. 151, No. 3-4. pp. 707-719.
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