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Brandes' algorithm: Algorithm, Definitions & Pseudocode

In network theory, Brandes' algorithm is an algorithm for calculating the betweenness centrality of vertices in a graph. The algorithm was first published in 2001 by Ulrik Brandes. Betweenness centrality, along with other measures of centrality, is an important measure in many real-world networks, such as social networks and computer networks.

Language: English [EN]
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Brandes' algorithm topic overview

The analysis highlights Algorithm, Definitions and Pseudocode as prominent areas in the source structure around Brandes' algorithm.

Related topics
17
Source areas
6
Connected nodes
23
Extracted relationships
9
Concept neighborhoods
15
Bridge connections
23

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.

Overview · 9 topics
Algorithm · 4 topics
Definitions · 1 topics
Pseudocode · 1 topics
Running time · 1 topics
Variants · 1 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.

Key facts & relationships

High-confidence facts extracted from structured source data. Use them as anchors for further research.

Class
Centrality Network theory
Data structure
Connected graph
Worst-case performance
O ( | V | | E | ) {\displaystyle O(|V||E|)} (unweighted) O ( | V | | E | + | V | 2 log ⁡ | V | ) {\displaystyle O(|V||E|+|V|^{2}\log |V|)} (weighted)
Worst-case space complexity
O ( | V | + | E | ) {\displaystyle O(|V|+|E|)}

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

Definitions

Algorithm

Pseudocode

Running time

Variants

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 Brandes' algorithm connects Entity context

The extracted context around Brandes' algorithm shows recurring relationship patterns in the source. For example, Brandes' algorithm → Brandes, For Another extracted example is Brandes' algorithm → Brandes, The. Use these groups to spot repeated connection types before inspecting the individual relationships.

Brandes' algorithm

Top relations

related to Algorithm · 2
Brandes' algorithm → Brandes, For
related to Pseudocode · 2
Brandes' algorithm → Brandes, The
Class · 1
Brandes' algorithm → Centrality Network theory
Data structure · 1
Brandes' algorithm → Connected graph
Worst-case performance · 1
Brandes' algorithm → O ( | V | | E | ) {\displaystyle O(|V||E|)} (unweighted) O ( | V | | E | + | V | 2 log ⁡ | V | ) {\displaystyle O(|V||E|+|V|^{2}\log |V|)} (weighted)
Worst-case space complexity · 1
Brandes' algorithm → O ( | V | + | E | ) {\displaystyle O(|V|+|E|)}
is a · 1
Brandes' algorithm → algorithm for calculating the betweenness centrality of vertices in a graph

Important terminology

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

Important terminology

displaystyle centrality vertex betweenness algorithm graph shortest vertices sigma sum delta breadth-first search brandes' path time backpropagation st number paths

Brandes' algorithm relationships Subject–Predicate–Object triples

TTTA extracted 9 structured relationships around Brandes' algorithm. Examples in this analysis include Brandes' algorithm → Class → Centrality Network theory and Brandes' algorithm → Data structure → Connected graph. The table shows each extracted connection, where it came from and its confidence.

SubjectPredicateObjectConfidenceSrc
Brandes' algorithmClassCentrality Network theory1.00infobox
Brandes' algorithmData structureConnected graph1.00infobox
Brandes' algorithmWorst-case performanceO ( | V | | E | ) {\displaystyle O(|V||E|)} (unweighted) O ( | V | | E | + | V | 2 log ⁡ | V | ) {\displaystyle O(|V||E|+|V|^{2}\log |V|)} (weighted)1.00infobox
Brandes' algorithmWorst-case space complexityO ( | V | + | E | ) {\displaystyle O(|V|+|E|)}1.00infobox
Brandes' algorithmis aalgorithm for calculating the betweenness centrality of vertices in a graph0.90text
Brandes' algorithmrelated to AlgorithmBrandes0.60section
Brandes' algorithmrelated to AlgorithmFor0.60section
Brandes' algorithmrelated to PseudocodeThe0.60section
Brandes' algorithmrelated to PseudocodeBrandes0.60section

Related concept clusters Concept neighborhoods

The concept neighborhoods around Brandes' algorithm bring nearby vocabulary together. In this analysis, examples include Algorithm, Brandes' and Complexity. Use the clusters to find adjacent concepts and terminology that may deserve separate research.

  • Brandes' algorithm
    • Algorithm
    • Brandes'
    • Complexity
    • Running
    • Space
    • Graph
    • Time
    • Betweenness
    • Centrality
    • Network
    • Theory
    • Data
  • brandes' algorithm
    • Algorithm
    • Brandes'
    • Complexity
    • Running
    • Space
    • Graph
    • Time
    • Network
    • Theory
    • Betweenness
    • Centrality
    • Data
  • algorithm
    • Brandes'
    • Complexity
    • Running
    • Space
    • Graph
    • Time
    • Network
    • Theory
    • Betweenness
    • Centrality
    • Data
    • Unweighted
  • betweenness centrality
    • Centrality
    • Graph
    • Network
    • Theory
    • Shortest
    • Connected
    • Node
    • Backpropagation
    • Brandes'
    • Path
    • Paths
    • Sum
  • vertices
    • Dependencies
    • Time
    • Displaystyle
    • Algorithm
    • Network
    • Theory
    • Vertex
    • Data
    • Edges
    • Running
    • Backpropagation
    • Edge
  • graph
    • Connected
    • Unweighted
    • Number
    • Network
    • Theory
    • Shortest
    • Edges
    • Node
    • Path
    • Since
    • Displaystyle
    • Vertex
  • centrality
    • Graph
    • Network
    • Theory
    • Shortest
    • Connected
    • Node
    • Backpropagation
    • Path
    • Paths
    • Sum
    • Displaystyle
    • Complexity
  • connected graph
    • Node
    • Connected
    • Graph
    • Number
    • Unweighted
    • Network
    • Theory
    • Shortest
    • Complexity
    • Data
    • Edges
    • Running

Connections between topic areas Semantic bridges

For Brandes' algorithm, one of the stronger structural bridges in this analysis connects Brandes' algorithm with Overview. 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
Brandes' algorithm — Overview · splits 14 ⟂ 10
Brandes' algorithm — Algorithm · splits 19 ⟂ 5

Map overview Semantic statistics

Brandes' algorithm

Nodes24
Edges23
Triples9
Avg. degree1.92
Density0.083333
Components1

Source & methodology

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

Source: Wikipedia — Brandes' algorithm · EN edition · Analysis: TopicsToTalkAbout

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