2007New Journal of PhysicsOpen access

A measure of centrality based on network efficiency

Vito Latora, Massimo Marchiori

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Abstract

We introduce delta centralities , a new class of measures of structural centrality for networks. In particular, we focus on a measure in this class, the information centrality C I , which is based on the concept of efficient propagation of information over the network. C I is defined for both valued and non-valued graphs, and applies to groups as well as individuals. The measure is illustrated and compared with respect to the standard centrality measures by using a classic network data set. The statistical distribution of information centrality is investigated by considering large computer generated graphs and two networks from the real world.

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We introduce delta centralities , a new class of measures of structural centrality for networks. In particular, we focus on a measure in this class, the information centrality C I , which is based on the concept of efficient propagation of information over the network. C I is defined for both valued and non-valued graphs, and applies to groups as well as individuals. The measure is illustrated and compared with respect to the standard centrality measures by using a classic network data set. The statistical distribution of information centrality is investigated by considering large computer generated graphs and two networks from the real world.

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Available abstract

We introduce delta centralities , a new class of measures of structural centrality for networks. In particular, we focus on a measure in this class, the information centrality C I , which is based on the concept of efficient propagation of information over the network. C I is defined for both valued and non-valued graphs, and applies to groups as well as individuals. The measure is illustrated and compared with respect to the standard centrality measures by using a classic network data set. The statistical distribution of information centrality is investigated by considering large computer generated graphs and two networks from the real world.

Key concepts: Centrality, Katz centrality, Measure (data warehouse), Class (philosophy), Set (abstract data type), Network theory, Physics, Network science

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