Let's assume I have the following graph:
A->B, B->D
A->C, C->D
A->E
I want to return all paths which start from A, but don't include multiple paths which end with the same vertex.
So in case, the result will be:
A->B->D (or A->C->D) and A->E
Since the graph is very big and there can be a lot of paths, if there is an efficient way the engine can prune the paths without collecting all of them first, it is preferable.
Depending upon how cyclic the data in the graph is, this can be an expensive query. I am assuming each eventual target is a leaf node. In such cases, the basic query might end up something like this:
g.V('A').
repeat(out().simplePath()).
until(not(out())).
where(without('x')).store('x').
path()
Related
Can someone help me please with this simple query...Many thanks in advance...
I am using the following gremlin query and it works well giving me the original vertex (v) (with id-=12345), its edges (e) and the child vertex (id property). However, say if the original vertex 'v' (with id-12345) has no outgoing edges, the query returns nothing. I still want the properties of the original vertex ('v') even if it has no outgoing edges and a child. How can I do that?
g.V().has('id', '12345').as('v').
outE().as('e').
inV().
as('child_v').
select('v', 'e', 'child_v').
by(valueMap()).by(id).by(id)
There are a couple of things going on here but the major update you need to the traversal is to use a project() step instead of a select().
select() and project() steps are similar in that they both allow you to format the results of a traversal however they differ in (at least) one significant way. select() steps function by allowing you to access previously traversed and labeled elements (via as). project() steps allow you take the current traverser and branch it to manipulate the output moving forward.
In your original traversal, when there are no outgoing edges from original v so all the traversers are filtered out during the outE() step. Since there are no further traversers after the outE() step then remainder of the traversal has no input stream so there is no data to return. If you use a project() step after the original v you're able to return the original traverser as well as return the edges and incident vertex. This does lead to a slight complication when handling cases where no out edges exist. Gremlin does not handle null values, such as no out edges existing, you need to return some constant value for these statements using a coalesce statement.
Here is functioning version of this traversal:
g.V().hasId(3).
project('v', 'e', 'child_v').
by(valueMap()).
by(coalesce(outE().id(), constant(''))).
by(coalesce(out().id(), constant('')))
Currently you will get a lot of duplicate data, in the above query you will get the vertex properties E times. probably will be better to use project:
g.V('12345').project('v', 'children').
by(valueMap()).
by(outE().as('e').
inV().as('child').
select('e', 'child').by(id).fold())
example: https://gremlify.com/a1
You can get the original data format if you do something like this:
g.V('12345').as('v').
coalesce(
outE().as('e').
inV().
as('child_v')
select('v', 'e', 'child_v').
by(valueMap()).by(id).by(id),
project('v').by(valueMap())
)
example: https://gremlify.com/a2
I want to create the initial population in a genetic algorithm. a population consists of paths between two nodes ( source and destination). how to find all possible paths between two nodes in an undirected graph?
Thanks
You could take a recursive approach to this problem. Do something along the lines of the following. (be warned I have not refined this).
Start by selecting a random node from the graph as a start node. And select a random node as the end node.
Look at all the connections to other nodes from the start one. Do not return to previous nodes. If there are no possible connections left stop.
If the node is the end node then stop and record the path. If not, then look at all the connections to that node, and repeat this step.
Repeat this process with every pair of nodes in the graph.
I'm sure you can see the recursive part to this solution. I'm afraid I cannot write up this solution currently but I hope this might point you in the right direction.
I'm executing a query similar to:
FOR v, e IN 1..10 ANY #start GRAPH #graph
FILTER e.someCondition
RETURN v
What I expected to happen was that if e.someCondition was false, then the edge in question wouldn't be traversed (and transitively, all other vertexes and edges reachable solely through e would never be visited).
However, it seems that what happens is that e is merely skipped and then traversal continues down that path.
So, how can I set boundaries on graph-traversal by edge-properties using AQL?
The query supports v, e and p, where p is the path it takes.
The ArangoDB documentation shows some examples.
I have used this to exclude specific nodes at specified depths in the path but you have to specify depth of nodes e.g. p.vertices[0].something != 'value').
Another thing you might want to look at is working with 'Custom Visitor' functions which are evaluated as the query traverses down a path.
This good blog post and this ArangoDB guide show some real world examples of it and it's well worth the read and effort to get a sample working. I've used these functions to summarise data in a path, aggregated by properties on the vertices in the path, but you can also use it to have custom paths followed.
This is worth the effort because it gives you huge flexibility over the computation it follows when traversing the graph. You can exclude branches, only include branches that meet specific requirements, or aggregate data about the paths it took.
I hope that helps.
I have a Neo4j graph with directed cycles. I have had no issue finding all descendants of A assuming I don't care about loops using this Cypher query:
match (n:TEST{name:"A"})-[r:MOVEMENT*]->(m:TEST)
return n,m,last(r).movement_time
The relationships between my nodes have a timestamp property on them, movement_time. I've simulated that in my test data below using numbers that I've imported as floats. I would like to traverse the graph using the timestamp as a constraint. Only follow relationships that have a greater movement_time than the movement_time of the relationship that brought us to this node.
Here is the CSV sample data:
from,to,movement_time
A,B,0
B,C,1
B,D,1
B,E,1
B,X,2
E,A,3
Z,B,5
C,X,6
X,A,7
D,A,7
Here is what the graph looks like:
I would like to calculate the descendants of every node in the graph and include the timestamp from the last relationship using Cypher; so I'd like my output data to look something like this:
Node:[{Descendant,Movement Time},...]
A:[{B,0},{C,1},{D,1},{E,1},{X,2}]
B:[{C,1},{D,1},{E,1},{X,2},{A,7}]
C:[{X,6},{A,7}]
D:[{A,7}]
E:[{A,3}]
X:[{A,7}]
Z:[{B,5}]
This non-Neo4J implementation looks similar to what I'm trying to do: Cycle enumeration of a directed graph with multi edges
This one is not 100% what you want, but very close:
MATCH (n:TEST)-[r:MOVEMENT*]->(m:TEST)
WITH n, m, r, [x IN range(0,length(r)-2) |
(r[x+1]).movement_time - (r[x]).movement_time] AS deltas
WHERE ALL (x IN deltas WHERE x>0)
RETURN n, collect(m), collect(last(r).movement_time)
ORDER BY n.name
We basically find all the paths between any of your nodes (beware cartesian products get very expensive on non-trivial datasets). In the WITH we're building a collection delta's that holds the difference between two subsequent movement_time properties.
The WHERE applies an ALL predicate to filter out those having any non-positive value - aka we guarantee increasing values of movement_time along the path.
The RETURN then just assembles the results - but not as a map, instead one collection for the reachable nodes and the last value of movement_time.
The current issue is that we have duplicates since e.g. there are multiple paths from B to A.
As a general notice: this problem is much more elegantly and more performant solvable by using Java traversal API (http://neo4j.com/docs/stable/tutorial-traversal.html). Here you would have a PathExpander that skips paths with decreasing movement_time early instead of collection all and filter out (as Cypher does).
I need to use Gremlin find the shortest path between two nodes (vertices) while avoiding a list of given vertices.
I already have:
v.bothE.bothV.loop(2){!it.object.equals(y)}.paths>>1
To get my shortest path.
I was attempting something like:
v.bothE.bothV.filter{it.name!="ignored"}.loop(3){!it.object.equals(y)}.paths>>1
but it does not seem to work.
Please HELP!!!
The second solution you have looks correct. However, to be clear on what you are trying to accomplish. If x and y are the vertices that you want to find the shortest path between and a vertex to ignore during the traversal if it has the property name:"ignored", then the query is:
x.both.filter{it.name!="ignored"}.loop(2){!it.object.equals(y)}.paths>>1
If the "list of given vertices" you want filtered is actually a list, then the traversal is described as such:
list = [ ... ] // construct some list
x.both.except(list).loop(2){!it.object.equals(y)}.paths>>1
Moreover, I tend to use a range filter just to be safe as this will go into an infinite loop if you forget the >>1 :)
x.both.except(list).loop(2){!it.object.equals(y)}[1].paths>>1
Also, if there is a potential for no path, then to avoid an infinitely long search, you can do a loop limit (e.g. no more than 4 steps):
x.both.except(list).loop(2){!it.object.equals(y) & it.loop < 5}.filter{it.object.equals(y)}.paths>>1
Note why the last filter step before paths is needed. There are two reasons the loop is broken out of. Thus, you might not be at y when you break out of the loop (instead, you broke out of the loop because it.loops < 5).
Here is you solution implemented over the Grateful Dead graph distributed with Gremlin. First some set up code, where we load the graph and define two vertices x and y:
gremlin> g = new TinkerGraph()
==>tinkergraph[vertices:0 edges:0]
gremlin> g.loadGraphML('data/graph-example-2.xml')
==>null
gremlin> x = g.v(89)
==>v[89]
gremlin> y = g.v(100)
==>v[100]
gremlin> x.name
==>DARK STAR
gremlin> y.name
==>BROWN EYED WOMEN
Now your traversal. Note that there is not name:"ignored" property, so instead, I altered it to account for the number of performances of each song along the path. Thus, shortest path of songs played more than 10 times in concert:
gremlin> x.both.filter{it.performances > 10}.loop(2){!it.object.equals(y)}.paths>>1
==>v[89]
==>v[26]
==>v[100]
If you use Gremlin 1.2+, then you can use a path closure to provide the names of those vertices (for example) instead of just the raw vertex objects:
gremlin> x.both.filter{it.performances > 10}.loop(2){!it.object.equals(y)}.paths{it.name}>>1
==>DARK STAR
==>PROMISED LAND
==>BROWN EYED WOMEN
I hope that helps.
Good luck!
Marko.