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Order in which the nodes are expanded
|Worst case performance|
|Worst case space complexity|
Breadth-first search (BFS) is an algorithm for traversing or searching tree or graph data structures. It starts at the tree root (or some arbitrary node of a graph) and explores the neighbor nodes first, before moving to the next level neighbours. Compare BFS with the equivalent, but more memory-efficient Iterative deepening depth-first search and contrast with depth-first search.
|Graph and tree
Input: A graph G and a vertex v of G
Output: All vertices reachable from v labeled as discovered
A non-recursive implementation of BFS:
1 procedure BFS(G,v) is 2 let Q be a queue 3 Q.push(v) 4 label v as discovered 5 while Q is not empty 6 v ← Q.pop() 7 for all edges from v to w in G.adjacentEdges(v) do 8 if w is not labeled as discovered 9 Q.push(w) 10 label w as discovered
The non-recursive implementation is similar to depth-first search but differs from it in two ways: it uses a queue instead of a stack, and it checks whether a vertex has been discovered before pushing the vertex rather than delaying this check until the vertex is popped from the stack.
Time and space complexity
The time complexity can be expressed as  since every vertex and every edge will be explored in the worst case. Note: may vary between and , depending on how sparse the input graph is.
When the number of vertices in the graph is known ahead of time, and additional data structures are used to determine which vertices have already been added to the queue, the space complexity can be expressed as where is the cardinality of the set of vertices. If the graph is represented by an Adjacency list it occupies  space in memory, while an Adjacency matrix representation occupies .
Completeness and optimality
In the analysis of algorithms, the input to breadth-first search is assumed to be a finite graph, represented explicitly as an adjacency list or similar representation. However, in the application of graph traversal methods in artificial intelligence the input may be an implicit representation of an infinite graph. In this context, a search method is described as being complete if it is guaranteed to find a goal state if one exists. Breadth-first search is complete, but depth-first search is not: when applied to infinite graphs represented implicitly, it may get lost in parts of the graph that have no goal state and never return.
A search method is optimal if it is guaranteed to find the best solution that exists. In other words, it will find the path to the goal state that involves the least number of steps. Breadth-first search is an optimal search method, but depth-first search is not
Breadth-first search can be used to solve many problems in graph theory, for example:
- Copying Collection, Cheney's algorithm
- Finding the shortest path between two nodes u and v (with path length measured by number of edges)
- Testing a graph for bipartiteness
- (Reverse) Cuthill–McKee mesh numbering
- Ford–Fulkerson method for computing the maximum flow in a flow network
- Serialization/Deserialization of a binary tree vs serialization in sorted order, allows the tree to be re-constructed in an efficient manner.
- Construction of the failure function of the Aho-Corasick pattern matcher.
BFS can be used to test bipartiteness, by starting the search at any vertex and giving alternating labels to the vertices visited during the search. That is, give label 0 to the starting vertex, 1 to all its neighbours, 0 to those neighbours' neighbours, and so on. If at any step a vertex has (visited) neighbours with the same label as itself, then the graph is not bipartite. If the search ends without such a situation occurring, then the graph is bipartite.
- Depth-first search
- Iterative deepening depth-first search
- Level structure
- Lexicographic breadth-first search
- Skiena, Steven (2008). The Algorithm Design Manual. Springer. p. 480. doi:10.1007/978-1-84800-070-4_4.
- Leiserson, Charles E.; Schardl, Tao B. (2010). A Work-Efficient Parallel Breadth-First Search Algorithm (or How to Cope with the Nondeterminism of Reducers). ACM Symp. on Parallelism in Algorithms and Architectures.
- Cormen, Thomas H., Charles E. Leiserson, and Ronald L. Rivest. p.597
- Cormen, Thomas H., Charles E. Leiserson, and Ronald L. Rivest. p.590
- Cormen, Thomas H., Charles E. Leiserson, and Ronald L. Rivest. p.591
- Coppin, B. (2004). Artificial intelligence illuminated. Jones & Bartlett Learning. pp. 79–80.
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