Breadth-first search

Use the mouse wheel or two fingers (on touchscreens) to zoom in and out of the map.

Export map as:

Research this topic

Explore the main themes, entities and connections around Breadth-first search. Start with the topic map, then use the sections below for research and deeper semantic analysis.

Explore this topic

Start with a few of the strongest sections from the source topic. These are research directions, not a list of keywords you must use.

Applications

Garbage collection Cheney's algorithm Shortest path (Reverse) Cuthill–McKee Ford–Fulkerson method

9 related topics

Overview

Computer science Algorithm Depth Queue Chess endgame

18 related topics

Analysis

Time complexity Space complexity Graph representation Branching factor Adjacency list

7 related topics

Pseudocode

Shortest path Queue First In First Out Stack German

5 related topics

Key facts & relationships

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

Class: Search algorithm
Data structure: Graph
Optimal: Yes (always finds shortest paths)
Worst-case performance: O ( | V | + | E | ) {\displaystyle O(|V|+|E|)}
Worst-case space complexity: O ( | V | ) {\displaystyle O(|V|)}

Topics to explore

Browse the full topic structure. Each item opens a new analysis centered on that subject.

Overview

Computer science
Algorithm
Depth Tree (data structure)
Queue Queue (data structure)
Chess endgame
Chess engine
Game tree
Depth-first search
Iterative deepening depth-first search
Undirected graphs Undirected graph
Directed graphs Directed graph
State space search
Artificial intelligence
Connected components Connected component (graph theory)
Konrad Zuse
Plankalkül
Edward F. Moore
Wire routing Routing (electronic design automation)

Pseudocode

Shortest path Shortest path problem
Queue Queue (abstract data type)
First In First Out FIFO (computing and electronics)
Stack Stack (abstract data type)
German Germany

Analysis

Time complexity
Space complexity
Graph representation Graph (abstract data type)
Branching factor
Adjacency list
Adjacency matrix
Implicit representation Implicit graph

Applications

Garbage collection Garbage collection (computer science)
Cheney's algorithm
Shortest path
(Reverse) Cuthill–McKee Cuthill–McKee algorithm
Ford–Fulkerson method Ford–Fulkerson algorithm
Maximum flow Maximum flow problem
Flow network
Aho-Corasick
Bipartiteness of a graph Bipartite graph

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.

Map overview Semantic statistics

Nodes44

Edges43

Triples30

Avg. degree1.95

Density0.045455

Components1

How this topic connects Entity context

See the strongest relationship patterns around the current topic before diving into the raw triples.

Breadth-first search

Top relations

has application · 12

Breadth-first search → Aho-Corasick, Breadth-first, Cheney's, Construction, Copying, Cuthill, Deserialization, Fulkerson, Implementing, McKee, Reverse, Testing

see also · 6

Breadth-first search → Algorithm, Depth-first, Object, Parallel, Partition-based, Tree

related to Completeness · 4

Breadth-first search → Breadth-first, However, In, When

related to External links · 3

Breadth-first search → Open Data Structures, Pat Morin, Section

Class · 1

Breadth-first search → Search algorithm

Data structure · 1

Breadth-first search → Graph

Optimal · 1

Breadth-first search → Yes (always finds shortest paths)

Worst-case performance · 1

Breadth-first search → O ( | V | + | E | ) {\displaystyle O(|V|+|E|)}

Worst-case space complexity · 1

Breadth-first search → O ( | V | ) {\displaystyle O(|V|)}

Important terminology Word statistics

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

Important terminology

search breadth-first graph tree bfs algorithm depth-first displaystyle nodes node may find queue shortest vertices space infinite graphs explored example

Entity relationships Subject–Predicate–Object triples

Subject	Predicate	Object	Confidence	Src
Breadth-first search	Class	Search algorithm	1.00	infobox
Breadth-first search	Data structure	Graph	1.00	infobox
Breadth-first search	Optimal	Yes (always finds shortest paths)	1.00	infobox
Breadth-first search	Worst-case performance	O ( \| V \| + \| E \| ) {\displaystyle O(\|V\|+\|E\|)}	1.00	infobox
Breadth-first search	Worst-case space complexity	O ( \| V \| ) {\displaystyle O(\|V\|)}	1.00	infobox
Breadth-first search	has application	Breadth-first	0.60	section
Breadth-first search	has application	Copying	0.60	section
Breadth-first search	has application	Cheney's	0.60	section
Breadth-first search	has application	Reverse	0.60	section
Breadth-first search	has application	Cuthill	0.60	section
Breadth-first search	has application	McKee	0.60	section
Breadth-first search	has application	Fulkerson	0.60	section

Related concept clusters Concept neighborhoods

These clusters group vocabulary that occurs around closely connected concepts in the source material.

Connections between topic areas Semantic bridges

Bridges can reveal useful research angles that are easy to miss in a flat list of related terms.

Min side: 3