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Order in which the nodes are expanded
|Worst case performance|
|Worst case space complexity|
In graph theory, breadth-first search (BFS) is a strategy for searching in a graph when search is limited to essentially two operations: (a) visit and inspect a node of a graph; (b) gain access to visit the nodes that neighbor the currently visited node. The BFS begins at a root node and inspects all the neighboring nodes. Then for each of those neighbor nodes in turn, it inspects their neighbor nodes which were unvisited, and so on. Compare BFS with the equivalent, but more memory-efficient Iterative deepening depth-first search and contrast with depth-first search.
|Graph and tree
The algorithm uses a queue data structure to store intermediate results as it traverses the graph, as follows:
- Enqueue the root node
- Dequeue a node and examine it
- If the element sought is found in this node, quit the search and return a result.
- Otherwise enqueue any successors (the direct child nodes) that have not yet been discovered.
- If the queue is empty, every node on the graph has been examined – quit the search and return "not found".
- If the queue is not empty, repeat from Step 2.
Input: A graph G and a root v of G
1 procedure BFS(G,v) is 2 create a queue Q 3 create a vector set V 4 enqueue v onto Q 5 add v to V 6 while Q is not empty loop 7 t ← Q.dequeue() 8 if t is what we are looking for then 9 return t 10 end if 11 for all edges e in G.adjacentEdges(t) loop 12 u ← G.adjacentVertex(t,e) 13 if u is not in V then 14 add u to V 15 enqueue u onto Q 16 end if 17 end loop 18 end loop 19 return none 20 end BFS
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 .
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 (assuming that the graph is connected).
Breadth-first search can be used to solve many problems in graph theory, for example:
- Finding all nodes within one connected component
- 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.
Finding connected components
The set of nodes reached by a BFS (breadth-first search) form the connected component containing the starting node.
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
- Cormen, Thomas H., Charles E. Leiserson, and Ronald L. Rivest. p.590
- Cormen, Thomas H., Charles E. Leiserson, and Ronald L. Rivest. p.591
- Cormen, Thomas H., Charles E. Leiserson, and Ronald L. Rivest. p.597
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