Graph traversal: Difference between revisions

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{{redirect-distinguish|Graph search|Facebook Graph Search}}

{{Short description|Computer science algorithm}}

{{refimprove|date=October 2014}}

{{Graph search algorithm}}

In [[computer science]], '''graph traversal''' (also known as '''graph search''') refers to the process of visiting (checking and/or updating) each vertex in a [[Graph (discrete mathematics)|graph]]. Such traversals are classified by the order in which the vertices are visited. [[Tree traversal]] is a special case of graph traversal.

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==Graph traversal algorithms==

Note. — If each vertex in a graph is to be traversed by a tree-based algorithm (such as DFS or BFS), then the algorithm must be called at least once for each [[connected component (graph theory)|connected component]] of the graph. This is easily accomplished by iterating through all the vertices of the graph, performing the algorithm on each vertex that is still unvisited when examined.

[[File:Graph-scan.png|thumb|A non-verbal description of three graph traversal algorithms: randomly, depth-first search, and breadth-first search.]]

===Depth-first search===

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* ''Output'': A labeling of the edges in the connected component of ''v'' as discovery edges and back edges.

1 '''procedure''' DFS(''G'', ''v''): '''is'''

2 label ''v'' as explored

3 '''for all''' edges ''e'' in ''G''.incidentEdges(''v'') '''do'''

4 '''if''' edge ''e'' is unexplored '''then'''

5 ''w'' ← ''G''.adjacentVertex(''v'', ''e'')

6 '''if''' vertex ''w'' is unexplored '''then'''

7 label ''e'' as a discovered edge

8 recursively call DFS(''G'', ''w'')

9 '''else'''

10 label ''e'' as a back edge

===Breadth-first search===

{{main|Breadth-first search}}

{{expand section|date=October 2012}}

A breadth-first search (BFS) is another technique for traversing a finite graph. BFS visits the neighborsibling vertices before visiting the child vertices, and a [[Queue (abstract data type)|queue]] is used in the search process. This algorithm is often used to find the shortest path from one vertex to another.

====Pseudocode====

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* ''Output'': The closest vertex to ''v'' satisfying some conditions, or null if no such vertex exists.

1 '''procedure''' BFS(''G'', ''v''): '''is'''

2 create a queue ''Q''

3 enqueue ''v'' onto ''Q''

4 mark ''v''

5 '''while''' ''Q'' is not empty: '''do'''

6 ''tw'' ← ''Q''.dequeue()

7 '''if''' ''tw'' is what we are looking for: '''then'''

8 return ''tw''

9 '''for all''' edges ''e'' in ''G''.adjacentEdges(''tw'') '''do'''

12 ''ox'' ← ''G''.adjacentVertex(''tw'', ''e'')

13 '''if''' ''ox'' is not marked: '''then'''

14 mark ''ox''

15 enqueue ''ox'' onto ''Q''

16 '''return''' null

==Applications==

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==Graph exploration==

The problem of graph exploration can be seen as a variant of graph traversal. It is an [[Online algorithm|online problem]], meaning that the information about the graph is only revealed during the runtime of the algorithm. A common model is as follows: given a connected graph {{nowrap|1=''G'' = (''V'', ''E'')}} with non-negative edge weights. The algorithm starts at some vertex, and knows all incident outgoing edges and the vertices at the end of these edges—but not more. When a new vertex is visited, then again all incident outgoing edges and the vertices at the end are known. The goal is to visit all ''n'' vertices and return to the starting vertex, but the sum of the weights of the tour should be as small as possible. The problem can also be understood as a specific version of the [[travelling salesman problem]], where the salesman has to discover the graph on the go.

For general graphs, the best known algorithms for both undirected and directed graphs is a simple [[greedy algorithm]]:

* In the undirected case, the greedy tour is at most {{nowrap|''O''(ln ''n'')}}-times longer than an optimal tour.<ref>{{cite journal|last1=Rosenkrantz|first1=Daniel J.|last2=Stearns|first2=Richard E.|last3=Lewis, II|first3=Philip M.|title=An Analysis of Several Heuristics for the Traveling Salesman Problem|journal=SIAM Journal on Computing|volume=6|issue=3|pages=563–581|doi=10.1137/0206041|year=1977|s2cid=14764079 }}</ref> The best lower bound known for any deterministic online algorithm is {{nowrap|210/3.5 − ''ε''}};<ref>{{cite journal |last1=DobrevBirx |first1=StefanAlexander |last2=KrálovicDisser |first2=RastislavYann |last3=MarkouHopp |first3=EuripidesAlexander V. |titlelast4=OnlineKarousatou Graph Exploration|first4=Christina with Advice|journaltitle=Proc.An ofimproved thelower 19thbound Internationalfor Colloquiumcompetitive ongraph Structuralexploration Information|journal=Theoretical andComputer CommunicationScience Complexity (SIROCCO)|date=2012May 2021 |volume=868 |pages=65–86 |doi=10.10071016/978-3-642-31104-8_23j.tcs.2021.04.003|arxiv=2002.10958 |s2cid=211296296 }}</ref>

** Unit weight undirected graphs can be explored with a competitive ration of {{nowrap|2 − ''ε''}},<ref>{{cite journal |last1=Miyazaki |first1=Shuichi |last2=Morimoto |first2=Naoyuki |last3=Okabe |first3=Yasuo |title=The Online Graph Exploration Problem on Restricted Graphs |journal=IEICE Transactions on Information and Systems |date=2009 |volume=E92-D |issue=9 |pages=1620–1627 |doi=10.1587/transinf.E92.D.1620|bibcode=2009IEITI..92.1620M |hdl=2433/226939 |s2cid=8355092 |hdl-access=free }}</ref> which is already a tight bound on [[Tadpole graph|Tadpole graphs]].<ref>{{cite journal |last1=Brandt |first1=Sebastian |last2=Foerster |first2=Klaus-Tycho |last3=Maurer |first3=Jonathan |last4=Wattenhofer |first4=Roger |title=Online graph exploration on a restricted graph class: Optimal solutions for tadpole graphs |journal=Theoretical Computer Science |date=November 2020 |volume=839 |pages=176–185 |doi=10.1016/j.tcs.2020.06.007|arxiv=1903.00581 |s2cid=67856035 }}</ref>

* In the directed case, the greedy tour is at most ({{nowrap|''n'' − 1}})-times longer than an optimal tour. This matches the lower bound of {{nowrap|''n'' − 1}}.<ref>{{cite journal|last1=Foerster|first1=Klaus-Tycho|last2=Wattenhofer|first2=Roger|title=Lower and upper competitive bounds for online directed graph exploration|journal=Theoretical Computer Science|date=December 2016|volume=655|pages=15–29|doi=10.1016/j.tcs.2015.11.017|url=https://zenodo.org/record/895821|doi-access=free}}</ref> An analogous competitive lower bound of ''Ω''(''n'') also holds for randomized algorithms that know the coordinates of each node in a geometric embedding. If instead of visiting all nodes just a single "treasure" node has to be found, the competitive bounds are ''Θ''(''n²''²) on unit weight directed graphs, for both deterministic and randomized algorithms.

==Universal traversal sequences==

{{expand section|date=December 2016}}

A '''universal traversal sequence''' is a sequence of instructions comprising a graph traversal for any [[regular graph]] with a set number of vertices and for any starting vertex. A probabilistic proof was used by Aleliunas et al. to show that there exists a universal traversal sequence with number of instructions proportional to <math display="inline">{{nowrap|''O\left''(''n^''<sup>5\right)</mathsup>)}} for any regular graph with ''n'' vertices.<ref>{{cite journalbook|last1=Aleliunas|first1=R.|last2=Karp|first2=R.|last3=Lipton|first3=R.|last4=Lovász|first4=L.|last5=Rackoff|first5=C. |title=20th Annual Symposium on Foundations of Computer Science (SFCS 1979) |chapter=Random walks, universal traversal sequences, and the complexity of maze problems |journalpages=20th Annual Symposium on Foundations of Computer Science (sfcs 1979)218–223|date=1979|doi=10.1109/SFCS.1979.34|s2cid=18719861 }}</ref> The steps specified in the sequence are relative to the current node, not absolute. For example, if the current node is ''v''_''j'', and ''v''_''j'' has ''d'' neighbors, then the traversal sequence will specify the next node to visit, ''v''_''j''+1, as the ''i''^th neighbor of ''v''_''j'', where 1 ≤ ''i'' ≤ ''d''.

==See also==

* [[External memory graph traversal]]

==References==

{{reflist}}

{{Graph traversal algorithms}}

{{Data structures and algorithms}}

[[Category:Graph algorithms]]