@article{
 title = {Some Unique Properties of Eigenvector Centrality},
 type = {article},
 year = {2007},
 identifiers = {[object Object]},
 keywords = {Centrality,Eigenvector},
 pages = {555-564},
 volume = {29},
 month = {10},
 id = {7d2187dd-8a2d-3ca6-9632-1b6ff32b28cd},
 created = {2018-09-07T14:41:21.438Z},
 file_attached = {false},
 profile_id = {77d22e2d-8db9-3a34-9de1-4de38737e3a3},
 group_id = {792e12b3-5024-3ff7-8089-6f69c89be240},
 last_modified = {2018-09-07T14:41:21.523Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {false},
 hidden = {false},
 source_type = {article},
 private_publication = {false},
 abstract = {Eigenvectors, and the related centrality measure Bonacich's c(β), have advantages over graph-theoretic measures like degree, betweenness, and closeness centrality: they can be used in signed and valued graphs and the beta parameter in c(β) permits the calculation of power measures for a wider variety of types of exchange. Degree, betweenness, and closeness centralities are defined only for classically simple graphsthose with strictly binary relations between vertices. Looking only at these classical graphs, where eigenvectors and graphtheoretic measures are competitors, eigenvector centrality is designed to be distinctively different from mere degree centrality when there are some high degree positions connected to many low degree others or some low degree positions are connected to a few high degree others. Therefore, it will not be distinctively different from degree when positions are all equal in degree (regular graphs) or in core-periphery structures in which high degree positions tend to be connected to each other.},
 bibtype = {article},
 author = {Bonacich, Phillip},
 journal = {Social Networks},
 number = {4}
}

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