Minimum Weight k-Spanning Tree
This feature is in the alpha tier. For more information on feature tiers, see API Tiers.
Glossary
- Directed
-
Directed trait. The algorithm is well-defined on a directed graph.
- Directed
-
Directed trait. The algorithm ignores the direction of the graph.
- Directed
-
Directed trait. The algorithm does not run on a directed graph.
- Undirected
-
Undirected trait. The algorithm is well-defined on an undirected graph.
- Undirected
-
Undirected trait. The algorithm ignores the undirectedness of the graph.
- Heterogeneous nodes
-
Heterogeneous nodes fully supported. The algorithm has the ability to distinguish between nodes of different types.
- Heterogeneous nodes
-
Heterogeneous nodes allowed. The algorithm treats all selected nodes similarly regardless of their label.
- Heterogeneous relationships
-
Heterogeneous relationships fully supported. The algorithm has the ability to distinguish between relationships of different types.
- Heterogeneous relationships
-
Heterogeneous relationships allowed. The algorithm treats all selected relationships similarly regardless of their type.
- Weighted relationships
-
Weighted trait. The algorithm supports a relationship property to be used as weight, specified via the relationshipWeightProperty configuration parameter.
- Weighted relationships
-
Weighted trait. The algorithm treats each relationship as equally important, discarding the value of any relationship weight.
Introduction
Sometimes, we might require a spanning tree(a tree where its nodes are connected with each via a single path) that does not necessarily span all nodes in the graph.
The K-Spanning tree heuristic algorithm returns a tree with k
nodes and k − 1
relationships.
Our heuristic processes the result found by Prim’s algorithm for the Minimum Weight Spanning Tree problem.
Like Prim, it starts from a given source node, finds a spanning tree for all nodes and then removes nodes using heuristics to produce a tree with 'k' nodes.
Note that the source node will not be necessarily included in the final output as the heuristic tries to find a globally good tree.
Considerations
The Minimum weight k-Spanning Tree is NP-Hard. The algorithm in the Neo4j GDS Library is therefore not guaranteed to find the optimal answer, but should hopefully return a good approximation in practice.
Like Prim algorithm, the algorithm focuses only on the component of the source node. If that component has fewer than k
nodes, it will not look into other components, but will instead return the component.
Syntax
This section covers the syntax used to execute the k-Spanning Tree heuristic algorithm in each of its execution modes. We are describing the named graph variant of the syntax. To learn more about general syntax variants, see Syntax overview.
CALL gds.kSpanningTree.write(
graphName: String,
configuration: Map
)
YIELD effectiveNodeCount: Integer,
preProcessingMillis: Integer,
computeMillis: Integer,
postProcessingMillis: Integer,
writeMillis: Integer,
configuration: Map
Name | Type | Default | Optional | Description |
---|---|---|---|---|
graphName |
String |
|
no |
The name of a graph stored in the catalog. |
configuration |
Map |
|
yes |
Configuration for algorithm-specifics and/or graph filtering. |
Name | Type | Default | Optional | Description |
---|---|---|---|---|
List of String |
|
yes |
Filter the named graph using the given node labels. Nodes with any of the given labels will be included. |
|
List of String |
|
yes |
Filter the named graph using the given relationship types. Relationships with any of the given types will be included. |
|
Integer |
|
yes |
The algorithm is single-threaded and changing the concurrency parameter has no effect on the runtime. |
|
String |
|
yes |
An ID that can be provided to more easily track the algorithm’s progress. |
|
Boolean |
|
yes |
If disabled the progress percentage will not be logged. |
|
Integer |
|
yes |
The number of concurrent threads used for writing the result to Neo4j. |
|
String |
|
no |
The node property in the Neo4j database to which the spanning tree is written. |
|
k |
Number |
|
no |
The size of the tree to be returned |
sourceNode |
Integer |
|
n/a |
The starting source node ID. |
String |
|
yes |
Name of the relationship property to use as weights. If unspecified, the algorithm runs unweighted. |
|
objective |
String |
|
yes |
If specified, the parameter dictates whether to seek a minimum or the maximum weight k-spanning tree. By default, the procedure looks for a minimum weight k-spanning tree. Permitted values are 'minimum' and 'maximum'. |
Name | Type | Description |
---|---|---|
effectiveNodeCount |
Integer |
The number of visited nodes. |
preProcessingMillis |
Integer |
Milliseconds for preprocessing the data. |
computeMillis |
Integer |
Milliseconds for running the algorithm. |
postProcessingMillis |
Integer |
Milliseconds for postprocessing results of the algorithm. |
writeMillis |
Integer |
Milliseconds for writing result data back. |
configuration |
Map |
The configuration used for running the algorithm. |
Minimum Weight k-Spanning Tree algorithm examples
In this section we will show examples of running the k-Spanning Tree heuristic algorithm on a concrete graph. The intention is to illustrate what the results look like and to provide a guide in how to make use of the algorithm in a real setting. We will do this on a small road network graph of a handful nodes connected in a particular pattern. The example graph looks like this:
CREATE (a:Place {id: 'A'}),
(b:Place {id: 'B'}),
(c:Place {id: 'C'}),
(d:Place {id: 'D'}),
(e:Place {id: 'E'}),
(f:Place {id: 'F'}),
(g:Place {id: 'G'}),
(d)-[:LINK {cost:4}]->(b),
(d)-[:LINK {cost:6}]->(e),
(b)-[:LINK {cost:1}]->(a),
(b)-[:LINK {cost:3}]->(c),
(a)-[:LINK {cost:2}]->(c),
(c)-[:LINK {cost:5}]->(e),
(f)-[:LINK {cost:1}]->(g);
MATCH (source:Place)
OPTIONAL MATCH (source)-[r:LINK]->(target:Place)
RETURN gds.graph.project(
'graph',
source,
target,
{ relationshipProperties: r { .cost } },
{ undirectedRelationshipTypes: ['*'] }
)
K-Spanning tree examples
Minimum K-Spanning Tree example
In our sample graph we have 7 nodes.
By setting the k=3
, we define that we want to find a 3-minimum spanning tree that covers 3 nodes and has 2 relationships.
MATCH (n:Place{id: 'A'})
CALL gds.kSpanningTree.write('graph', {
k: 3,
sourceNode: n,
relationshipWeightProperty: 'cost',
writeProperty:'kmin'
})
YIELD preProcessingMillis, computeMillis, writeMillis, effectiveNodeCount
RETURN preProcessingMillis,computeMillis,writeMillis, effectiveNodeCount;
MATCH (n)
WITH n.kmin AS p, count(n) AS c
WHERE c = 3
MATCH (n)
WHERE n.kmin = p
RETURN n.id As Place, p as Partition
Place | Partition |
---|---|
"A" |
0 |
"B" |
0 |
"C" |
0 |
Nodes A, B, and C form the discovered 3-minimum spanning tree of our graph.
Maximum K-Spanning Tree example
MATCH (n:Place{id: 'D'})
CALL gds.kSpanningTree.write('graph', {
k: 3,
sourceNode: n,
relationshipWeightProperty: 'cost',
writeProperty:'kmax',
objective: 'maximum'
})
YIELD preProcessingMillis, computeMillis, writeMillis, effectiveNodeCount
RETURN preProcessingMillis,computeMillis,writeMillis, effectiveNodeCount;
MATCH (n)
WITH n.kmax AS p, count(n) AS c
WHERE c = 3
MATCH (n)
WHERE n.kmax = p
RETURN n.id As Place, p as Partition
Place | Partition |
---|---|
"C" |
4 |
"D" |
4 |
"E" |
4 |
Nodes C, D, and E form a 3-maximum spanning tree of our graph.