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Lexicographic breadth-first search: Applications, Standards & Science

In computer science, lexicographic breadth-first search or Lex-BFS is a linear time algorithm for ordering the vertices of a graph. The algorithm is different from a breadth-first search, but it produces an ordering that is consistent with breadth-first search.

Language: English [EN]
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Lexicographic breadth-first search topic overview

The analysis highlights Applications, Standards and Science as prominent areas in the source structure around Lexicographic breadth-first search.

Related topics
27
Source areas
4
Connected nodes
31
Extracted relationships
28
Concept neighborhoods
21
Bridge connections
31

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 · 11 topics
Algorithm · 7 topics
Applications · 6 topics
Background · 3 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

Background

Algorithm

Applications

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 Lexicographic breadth-first search connects Entity context

The extracted context around Lexicographic breadth-first search shows recurring relationship patterns in the source. For example, Lexicographic breadth-first search → At, Find, For, If, In, Initialize, Move, The, Then, While Another extracted example is Lexicographic breadth-first search → Continue, GFor, If, In, Let, Therefore, Use. Use these groups to spot repeated connection types before inspecting the individual relationships.

Lexicographic breadth-first search

Top relations

related to Algorithm · 10
Lexicographic breadth-first search → At, Find, For, If, In, Initialize, Move, The, Then, While
related to Chordal graphs · 7
Lexicographic breadth-first search → Continue, GFor, If, In, Let, Therefore, Use
has application · 3
Lexicographic breadth-first search → As, Bretscher, Habib
related to Graph coloring · 2
Lexicographic breadth-first search → An, For

Important terminology

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

Important terminology

search graph breadth-first ordering algorithm vertices lexicographic sequence vertex set chordal output time graphs empty linear first used coloring queue

Lexicographic breadth-first search relationships Subject–Predicate–Object triples

TTTA extracted 28 structured relationships around Lexicographic breadth-first search. Examples in this analysis include breadth-first search → instance of → like simpler graph search algorithms and Lexicographic breadth-first search → has application → Bretscher. The table shows each extracted connection, where it came from and its confidence.

SubjectPredicateObjectConfidenceSrc
breadth-first searchinstance oflike simpler graph search algorithms0.80text
depth-first searchinstance oflike simpler graph search algorithms0.80text
this algorithm takes linear time.The algorithm is called lexicographic breadth-first search because the order it produces is an ordering that could also have been produced by a breadth-first searchinstance oflike simpler graph search algorithms0.80text
and because if the ordering is used to index the rowsinstance oflike simpler graph search algorithms0.80text
columns of an adjacency matrix of a graph then the algorithm sorts the rowsinstance oflike simpler graph search algorithms0.80text
columns into lexicographical orderinstance oflike simpler graph search algorithms0.80text
Lexicographic breadth-first searchhas applicationBretscher0.60section
Lexicographic breadth-first searchhas applicationAs0.60section
Lexicographic breadth-first searchhas applicationHabib0.60section
Lexicographic breadth-first searchrelated to AlgorithmThe0.60section
Lexicographic breadth-first searchrelated to AlgorithmAt0.60section
Lexicographic breadth-first searchrelated to AlgorithmThen0.60section

Related concept clusters Concept neighborhoods

The concept neighborhoods around Lexicographic breadth-first search bring nearby vocabulary together. In this analysis, examples include Search, Lexicographic and Ordering. Use the clusters to find adjacent concepts and terminology that may deserve separate research.

  • Lexicographic breadth-first search
    • Search
    • Lexicographic
    • Ordering
    • Algorithm
    • Graph
    • Order
    • Perfect
    • Standard
    • First
    • Graphs
    • Linear
    • Used
  • lexicographic breadth-first search
    • Search
    • Lexicographic
    • Algorithm
    • Ordering
    • Standard
    • Graph
    • Order
    • Graphs
    • Linear
    • Time
    • Perfect
    • First
  • vertices
    • Algorithm
    • Sequence
    • Graph
    • Queue
    • Ordering
    • Two
    • Vertex
    • Breadth-first
    • Output
    • Search
    • Coloring
    • Earlier
  • graph
    • Ordering
    • Chordal
    • Search
    • Lexicographic
    • Coloring
    • Elimination
    • Perfect
    • Used
    • Vertices
    • Applications
    • Lexbfs
    • Vertex
  • breadth-first search
    • Search
    • Lexicographic
    • Algorithm
    • Ordering
    • Standard
    • Graph
    • Order
    • Graphs
    • Linear
    • Time
    • Rule
    • Queue
  • lexicographic order
    • Search
    • Ordering
    • Algorithm
    • Standard
    • Two
    • Graph
    • Order
    • Perfect
    • First
    • Graphs
    • Linear
    • Used
  • depth-first search
    • Ordering
    • Standard
    • Order
    • Graphs
    • Time
    • Rule
    • Queue
    • Vertex
    • First
    • Used
    • Chordal
    • Vertices
  • complement graph
    • Ordering
    • Chordal
    • Search
    • Lexicographic
    • Coloring
    • Elimination
    • Perfect
    • Used
    • Vertices
    • Applications
    • Lexbfs
    • Vertex

Connections between topic areas Semantic bridges

For Lexicographic breadth-first search, one of the stronger structural bridges in this analysis connects Lexicographic breadth-first search 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
Lexicographic breadth-first search — Overview · splits 20 ⟂ 12
Lexicographic breadth-first search — Algorithm · splits 24 ⟂ 8
Lexicographic breadth-first search — Applications · splits 25 ⟂ 7
Lexicographic breadth-first search — Background · splits 28 ⟂ 4

Map overview Semantic statistics

Lexicographic breadth-first search

Nodes32
Edges31
Triples28
Avg. degree1.94
Density0.0625
Components1

Source & methodology

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

Source: Wikipedia — Lexicographic breadth-first search · EN edition · Analysis: TopicsToTalkAbout

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