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Eigenvector Centrality
Eigenvector centrality, introduced by Bonacich in 1972, measures a node's influence by considering not just how many neighbors it has, but how influential those neighbors are. A node scores highly if it is connected to other high-scoring nodes, making it a recursive, globally-aware measure of structural importance in a network.
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Sources
- Bonacich, P. (1972). Factoring and weighting approaches to status scores and clique identification. Journal of Mathematical Sociology, 2(1), 113–120. DOI: 10.1080/0022250X.1972.9989806 ↗
- Eigenvector centrality. Wikipedia. link ↗
Related methods
Referenced by
Bayesian PageRankBetweenness CentralityCloseness CentralityDegree CentralityDirected Eigenvector CentralityDirected Knowledge Graph AnalysisDirected PageRankDynamic Eigenvector CentralityDynamic PageRankModularity AnalysisMultilayer PageRankNetwork Diffusion AnalysisSocial Network AnalysisTemporal Eigenvector CentralityWeighted Closeness CentralityWeighted Degree CentralityWeighted Eigenvector CentralityWeighted PageRank