Research any topic before you write.

Find related topics. | Discover entities. | See connections. | Build a topical map.

Betweenness centrality: Works & Applications

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…

Language: English [EN]
Use the mouse wheel or two fingers (on touchscreens) to zoom in and out of the map.
100%
More settings
100% 100% 100% 100% 100%

Betweenness centrality topic overview

The analysis highlights Works and Applications as prominent areas in the source structure around Betweenness centrality.

Related topics
40
Source areas
7
Connected nodes
47
Extracted relationships
42
Concept neighborhoods
32
Bridge connections
47

What this topic covers Research coverage

Source areas are shown by the number of related topics found in each part of the analysis. Use smaller areas too: they can reveal specialized angles and content gaps.

Algorithms · 14 topics
Overview · 10 topics
Applications · 6 topics
Definition · 3 topics
Percolation centrality · 3 topics
Related concepts · 2 topics
Weighted networks · 2 topics

Smaller areas are not necessarily less important. They contain fewer connections in this analysis and can be useful for finding specialized angles or coverage gaps.

Explore all related topics Closing gaps

Browse the complete topic structure, not only the most central items. Less prominent entities and concepts can reveal missing angles, specialized context and useful research gaps. Each item opens a new analysis centered on that subject.

Overview

Definition

Weighted networks

Percolation centrality

Algorithms

Applications

Related concepts

Advanced semantic analysis

Deeper signals for content research, entity SEO and topical coverage. The plain-language headings explain what each technical view is useful for.

How Betweenness centrality connects Entity context

The extracted context around Betweenness centrality shows recurring relationship patterns in the source. For example, Betweenness centrality → Computer, For, Hossain, In, PC, Percolation, Piraveenan, Prokopenko, Rumours, The, This Another extracted example is Betweenness centrality → ABRA, Because, Chervonenkis, Kornaropoulos, Later, Many, Rademacher, Riondato, SILVAN, Vapnik. Use these groups to spot repeated connection types before inspecting the individual relationships.

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

Use these terms to understand the vocabulary surrounding the topic, not as a checklist for keyword stuffing.

Important terminology

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

Betweenness centrality relationships Subject–Predicate–Object triples

TTTA extracted 42 structured relationships around Betweenness centrality. Examples in this analysis include Betweenness centrality → is a → measure of centrality in a graph based on shortest paths and ABRA → instance of → Later methods. The table shows each extracted connection, where it came from and its confidence.

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

Related concept clusters Concept neighborhoods

The concept neighborhoods around Betweenness centrality bring nearby vocabulary together. In this analysis, examples include Centrality, Shortest and Vertices. Use the clusters to find adjacent concepts and terminology that may deserve separate research.

  • Betweenness centrality
    • Centrality
    • Shortest
    • Vertices
    • Paths
    • Percolation
    • Calculating
    • Graph
    • Graphs
    • Nodes
    • Displaystyle
    • Network
    • Pairs
  • betweenness centrality
    • Centrality
    • Shortest
    • Vertices
    • Paths
    • Percolation
    • Calculating
    • Measures
    • Graph
    • Graphs
    • Node
    • Nodes
    • Displaystyle
  • graph theory
    • Based
    • Edges
    • Connected
    • Shortest
    • Time
    • Vertices
    • Calculating
    • One
    • Path
    • Betweenness
    • Graphs
    • Measure
  • centrality
    • Percolation
    • Measures
    • Shortest
    • Node
    • Nodes
    • Displaystyle
    • Network
    • Brandes'
    • Path
    • Social
    • Graphs
    • Algorithm
  • graph
    • Edges
    • Connected
    • Shortest
    • Time
    • Vertices
    • Calculating
    • One
    • Path
    • Betweenness
    • Graphs
    • Algorithm
    • Nodes
  • shortest paths
    • Paths
    • Shortest
    • Path
    • Vertices
    • Node
    • One
    • Number
    • Nodes
    • Percolated
    • Percolation
    • Target
    • Weighted
  • connected graph
    • Edges
    • Connected
    • Graph
    • Graphs
    • Shortest
    • Time
    • Vertices
    • Calculating
    • One
    • Path
    • Betweenness
    • Algorithm
  • network theory
    • Based
    • Measure
    • Nodes
    • Related
    • Measures
    • Network
    • One
    • Sampling
    • Theory
    • People
    • Weighted
    • Proposed

Connections between topic areas Semantic bridges

For Betweenness centrality, one of the stronger structural bridges in this analysis connects Betweenness centrality with Algorithms. Bridges highlight paths between different parts of the map and can reveal research angles that are easy to miss in a flat list.

Min side: 3
Betweenness centralityAlgorithms · splits 33 ⟂ 15
Betweenness centralityOverview · splits 37 ⟂ 11
Betweenness centralityApplications · splits 41 ⟂ 7
Betweenness centralityDefinition · splits 44 ⟂ 4
Betweenness centralityPercolation centrality · splits 44 ⟂ 4
Betweenness centralityWeighted networks · splits 45 ⟂ 3
Betweenness centralityRelated concepts · splits 45 ⟂ 3

Map overview Semantic statistics

Betweenness centrality

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

Source & methodology

TTTA analyzes the structure around Betweenness centrality to surface related topics, entities, relationships, concept neighborhoods and bridge connections. Use the map to explore areas such as Works & Applications, including less central topics that may reveal useful research gaps. Automatically extracted connections are research leads rather than rewritten encyclopedia content.

Source: Wikipedia — Betweenness centrality · EN edition · Analysis: TopicsToTalkAbout

For writers, content strategists, SEOs, marketers and creators — from quick topic research to advanced semantic analysis.