k Shortest Paths¶
General idea¶
This type of query is supposed to find the first k paths in order of length (or weight) between two given documents, startVertex and targetVertex in your graph.
Every such path will be returned as a JSON object with three components:
 an array containing the
vertices
on the path  an array containing the
edges
on the path  the
weight
of the path, that is the sum of all edge weights
If no weightAttribute is given, the weight of the path is just its length.
Example
Let us take a look at a simple example to explain how it works. This is the graph that we are going to find some shortest path on:
Each ellipse stands for a train station with the name of the city written inside of it. They are the vertices of the graph. Arrows represent train connections between cities and are the edges of the graph. The numbers near the arrows describe how long it takes to get from one station to another. They are used as edge weights.
Let us assume that we want to go from Aberdeen to London by train.
We expect to see the following vertices on the shortest path, in this order:
 Aberdeen
 Leuchars
 Edinburgh
 York
 London
By the way, the weight of the path is: 1.5 + 1.5 + 3.5 + 1.8 = 8.3.
Let us look at alternative paths next, for example because we know that the direct connection between York and London does not operate currently.
An alternative path, which is slightly longer, goes like this:
 Aberdeen
 Leuchars
 Edinburgh
 York
 Carlisle
 Birmingham
 London
Its weight is: 1.5 + 1.5 + 3.5 + 2.0 + 1.5 = 10.0.
Another route goes via Glasgow. There are seven stations on the path as well, however, it is quicker if we compare the edge weights:
 Aberdeen
 Leuchars
 Edinburgh
 Glasgow
 Carlisle
 Birmingham
 London
The path weight is lower: 1.5 + 1.5 + 1.0 + 1.0 + 2.0 + 1.5 = 8.5.
Syntax¶
The syntax for k Shortest Paths queries is similar to the one for Shortest Path and there are also two options to either use a named graph or a set of edge collections. It only emits a path variable however, whereas SHORTEST_PATH emits a vertex and an edge variable.
Note
It is highly recommended that you use a LIMIT statement, as k Shortest Paths is a potentially expensive operation. On large connected graphs it can return a large number of paths, or perform an expensive (but unsuccessful) search for more short paths.
Working with named graphs¶
FOR path
IN OUTBOUNDINBOUNDANY K_SHORTEST_PATHS
startVertex TO targetVertex
GRAPH graphName
[OPTIONS options]
[LIMIT offset, count]

FOR
: emits the variable path which contains one path as an object containingvertices
,edges
, and theweight
of the path. 
IN
OUTBOUNDINBOUNDANY
: defines in which direction edges are followed (outgoing, incoming, or both) 
K_SHORTEST_PATHS
: the keyword to compute k Shortest Paths startVertex
TO
targetVertex (both stringobject): the two vertices between which the paths will be computed. This can be specified in the form of a ID string or in the form of a document with the attribute_id
. All other values will lead to a warning and an empty result. If one of the specified documents does not exist, the result is empty as well and there is no warning.
 startVertex

GRAPH
graphName (string): the name identifying the named graph. Its vertex and edge collections will be looked up. 
OPTIONS
options (object, optional): used to modify the execution of the traversal. Only the following attributes have an effect, all others are ignored: weightAttribute (string): a toplevel edge attribute that should be used to read the edge weight. If the attribute does not exist or is not numeric, the defaultWeight will be used instead.
 defaultWeight (number): this value will be used as fallback if there is no weightAttribute in the edge document, or if it's not a number. The default is 1.

LIMIT
(see LIMIT operation, optional): the maximal number of paths to return. It is highly recommended to use aLIMIT
forK_SHORTEST_PATHS
.
Working with collection sets¶
FOR path
IN OUTBOUNDINBOUNDANY K_SHORTEST_PATHS
startVertex TO targetVertex
edgeCollection1, ..., edgeCollectionN
[OPTIONS options]
[LIMIT offset, count]
Instead of GRAPH graphName
you can specify a list of edge collections. The involved vertex collections are determined by the edges of the given edge collections.
Traversing in mixed directions¶
For k shortest paths with a list of edge collections you can optionally specify the direction for some of the edge collections. Say for example you have three edge collections edges1, edges2 and edges3, where in edges2 the direction has no relevance, but in edges1 and edges3 the direction should be taken into account. In this case you can use OUTBOUND
as general search direction and ANY
specifically for edges2 as follows:
FOR vertex IN OUTBOUND K_SHORTEST_PATHS
startVertex TO targetVertex
edges1, ANY edges2, edges3
All collections in the list that do not specify their own direction will use the direction defined after IN
(here: OUTBOUND
). This allows to use a different direction for each collection in your path search.
Examples¶
We load an example graph to get a named graph that reflects some possible train connections in Europe and North America.
Suppose we want to query a route from Aberdeen to London, and compare the outputs of SHORTEST_PATH
and K_SHORTEST_PATHS
with LIMIT 1
. Note that while SHORTEST_PATH
and K_SHORTEST_PATH
with LIMIT 1
should return a path of the same length (or weight), they do not need to return the same path.
Using SHORTEST_PATH
:
FOR v, e IN OUTBOUND SHORTEST_PATH 'places/Aberdeen' TO 'places/London'
GRAPH 'kShortestPathsGraph'
RETURN { place: v.label, travelTime: e.travelTime }
Results:
[
{
"place": "Aberdeen",
"travelTime": null
},
{
"place": "Leuchars",
"travelTime": 1.5
},
{
"place": "Edinburgh",
"travelTime": 1.5
},
{
"place": "York",
"travelTime": 3.5
},
{
"place": "London",
"travelTime": 1.8
}
]
Using K_SHORTEST_PATHS
:
FOR p IN OUTBOUND K_SHORTEST_PATHS 'places/Aberdeen' TO 'places/London'
GRAPH 'kShortestPathsGraph'
LIMIT 1
RETURN { places: p.vertices[*].label, travelTimes: p.edges[*].travelTime }
Results:
[
{
"places": [
"Aberdeen",
"Leuchars",
"Edinburgh",
"York",
"London"
],
"travelTimes": [
1.5,
1.5,
3.5,
1.8
]
}
]
With K_SHORTEST_PATHS
we can ask for more than one option for a route:
FOR p IN OUTBOUND K_SHORTEST_PATHS 'places/Aberdeen' TO 'places/London'
GRAPH 'kShortestPathsGraph'
LIMIT 3
RETURN {
places: p.vertices[*].label,
travelTimes: p.edges[*].travelTime,
travelTimeTotal: SUM(p.edges[*].travelTime)
}
Results:
[
{
"places": [
"Aberdeen",
"Leuchars",
"Edinburgh",
"York",
"London"
],
"travelTimes": [
1.5,
1.5,
3.5,
1.8
],
"travelTimeTotal": 8.3
},
{
"places": [
"Aberdeen",
"Leuchars",
"Edinburgh",
"York",
"Carlisle",
"Birmingham",
"London"
],
"travelTimes": [
1.5,
1.5,
3.5,
3.5,
2,
1.5
],
"travelTimeTotal": 13.5
},
{
"places": [
"Aberdeen",
"Leuchars",
"Edinburgh",
"Glasgow",
"Carlisle",
"York",
"London"
],
"travelTimes": [
1.5,
1.5,
1,
1,
2.5,
1.8
],
"travelTimeTotal": 9.3
}
]
If we ask for routes that don't exist we get an empty result (from Aberdeen to Toronto):
FOR p IN OUTBOUND K_SHORTEST_PATHS 'places/Aberdeen' TO 'places/Toronto'
GRAPH 'kShortestPathsGraph'
LIMIT 3
RETURN {
places: p.vertices[*].label,
travelTimes: p.edges[*].travelTime,
travelTimeTotal: SUM(p.edges[*].travelTime)
}
Results:
[]
We can use the attribute travelTime that connections have as edge weights to take into account which connections are quicker. A high default weight is set, to be used if an edge has no travelTime attribute (not the case with the example graph). This returns the top three routes with the fewest changes and favoring the least travel time for the connection Saint Andrews to Cologne:
FOR p IN OUTBOUND K_SHORTEST_PATHS 'places/StAndrews' TO 'places/Cologne'
GRAPH 'kShortestPathsGraph'
OPTIONS {
weightAttribute: 'travelTime',
defaultWeight: 15
}
LIMIT 3
RETURN {
places: p.vertices[*].label,
travelTimes: p.edges[*].travelTime,
travelTimeTotal: SUM(p.edges[*].travelTime)
}
Results:
[
{
"places": [
"StAndrews",
"Leuchars",
"Edinburgh",
"York",
"London",
"Brussels",
"Cologne"
],
"travelTimes": [
0.2,
1.5,
3.5,
1.8,
2.5,
2
],
"travelTimeTotal": 11.5
},
{
"places": [
"StAndrews",
"Leuchars",
"Edinburgh",
"Glasgow",
"Carlisle",
"Birmingham",
"London",
"Brussels",
"Cologne"
],
"travelTimes": [
0.2,
1.5,
1,
1,
2,
1.5,
2.5,
2
],
"travelTimeTotal": 11.7
},
{
"places": [
"StAndrews",
"Leuchars",
"Edinburgh",
"Glasgow",
"Carlisle",
"York",
"London",
"Brussels",
"Cologne"
],
"travelTimes": [
0.2,
1.5,
1,
1,
2.5,
1.8,
2.5,
2
],
"travelTimeTotal": 12.5
}
]
And finally clean up by removing the named graph.