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Betweenness centrality

In graph theory, betweenness centrality is a measure of centrality in a graph based on shortest paths. Betweenness centrality measures how frequently a node appears on the shortest path between other nodes in the graph. For every pair of vertices in a connected graph, there exists at least one shortest path between the vertices, that is, there exists at…

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Overview

Definition

Weighted networks

Percolation centrality

Algorithms

Applications

Related concepts

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Map overview Semantic statistics

Betweenness centrality

Nodes48
Edges47
Triples42
Avg. degree1.96
Density0.041667
Components1

How this topic connects Entity context

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Betweenness centrality

Top relations

related to Percolation centrality · 11
Betweenness centrality → Computer, For, Hossain, In, PC, Percolation, Piraveenan, Prokopenko, Rumours, The, This
related to Approximations · 10
Betweenness centrality → ABRA, Because, Chervonenkis, Kornaropoulos, Later, Many, Rademacher, Riondato, SILVAN, Vapnik
related to Algorithms · 9
Betweenness centrality → Brandes, Calculating, Floyd, In, Johnson's, On, Theta, Warshall, When
related to Social networks · 2
Betweenness centrality → From, In
is a · 1
Betweenness centrality → measure of centrality in a graph based on shortest paths
related to Definition · 1
Betweenness centrality → The
related to Related concepts · 1
Betweenness centrality → Betweenness
related to River networks · 1
Betweenness centrality → Betweenness

Important terminology Word statistics

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Important terminology

centrality betweenness nodes node shortest network displaystyle networks vertices number graph graphs paths percolation path time algorithm social edges theory

Entity relationships Subject–Predicate–Object triples

SubjectPredicateObjectConfidenceSrc
Betweenness centralityis ameasure of centrality in a graph based on shortest paths0.90text
ABRAinstance ofLater methods0.80text
SILVAN used progressive sampling strategiesinstance ofLater methods0.80text
Rademacher averages to adaptively determine the number of sampled shortest paths needed to achieve a target accuracy.KADABRA is an adaptive approximation algorithm that combines shortest-path sampling with bidirectional breadth-first searchinstance ofLater methods0.80text
confidence interval estimation.Local heuristics have also been proposed as computationally inexpensive alternativesinstance ofLater methods0.80text
degreeinstance ofuse only local structural properties0.80text
the clustering coefficient to estimate the relative importance of verticesinstance ofuse only local structural properties0.80text
Betweenness centralityrelated to AlgorithmsCalculating0.60section
Betweenness centralityrelated to AlgorithmsTheta0.60section
Betweenness centralityrelated to AlgorithmsFloyd0.60section
Betweenness centralityrelated to AlgorithmsWarshall0.60section
Betweenness centralityrelated to AlgorithmsOn0.60section

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