K1 Coloring
This feature is in the beta tier. For more information on feature tiers, see API Tiers.
Glossary
 Directed

Directed trait. The algorithm is welldefined 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 welldefined 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.
1. Introduction
The K1 Coloring algorithm assigns a color to every node in the graph, trying to optimize for two objectives:

To make sure that every neighbor of a given node has a different color than the node itself.

To use as few colors as possible.
Note that the graph coloring problem is proven to be NPcomplete, which makes it intractable on anything but trivial graph sizes. For that reason the implemented algorithm is a greedy algorithm. Thus it is neither guaranteed that the result is an optimal solution, using as few colors as theoretically possible, nor does it always produce a correct result where no two neighboring nodes have different colors. However the precision of the latter can be controlled by the number of iterations this algorithm runs.
For more information on this algorithm, see:
Running this algorithm requires sufficient memory availability. Before running this algorithm, we recommend that you read Memory Estimation. 
2. Syntax
CALL gds.beta.k1coloring.stream(graphName: String, configuration: Map)
YIELD nodeId, color
Name  Type  Default  Optional  Description 

graphName 
String 

yes 
The name of an existing graph on which to run the algorithm. If no graph name is provided, the configuration map must contain configuration for creating a graph. 
configuration 
Map 

yes 
Additional configuration, see below. 
Name  Type  Default  Optional  Description 

concurrency 
Integer 
4 
yes 
The number of concurrent threads used for running the algorithm. Also provides the default value for 'readConcurrency' and 'writeConcurrency'. This is dependent on the Neo4j edition; for more information, see CPU. 
Integer 
10 
yes 
The maximum number of iterations of K1 Coloring to run. 

minCommunitySize 
Integer 
0 
yes 
Only nodes inside communities larger or equal the given value are returned. 
Name  Type  Description 

nodeId 
Integer 
The ID of the Node 
color 
Integer 
The color of the Node 
CALL gds.beta.k1coloring.stats(
graphName: String,
configuration: Map
)
YIELD
nodeCount,
colorCount,
ranIterations,
didConverge,
configuration,
preProcessingMillis,
computeMillis
Name  Type  Default  Optional  Description 

graphName 
String 

no 
The name of a graph stored in the catalog. 
configuration 
Map 

yes 
Configuration for algorithmspecifics and/or graph filtering. 
Name  Type  Default  Optional  Description 

concurrency 
Integer 
4 
yes 
The number of concurrent threads used for running the algorithm. Also provides the default value for 'readConcurrency' and 'writeConcurrency'. This is dependent on the Neo4j edition; for more information, see CPU. 
Integer 
10 
yes 
The maximum number of iterations of K1 Coloring to run. 
Name  Type  Description 

nodeCount 
Integer 
The number of nodes considered. 
ranIterations 
Integer 
The actual number of iterations the algorithm ran. 
didConverge 
Boolean 
An indicator of whether the algorithm found a correct coloring. 
colorCount 
Integer 
The number of colors used. 
preProcessingMillis 
Integer 
Milliseconds for preprocessing the data. 
computeMillis 
Integer 
Milliseconds for running the algorithm. 
configuration 
Map 
The configuration used for running the algorithm. 
CALL gds.beta.k1coloring.mutate(graphName: String, configuration: Map)
YIELD nodeCount, colorCount, ranIterations, didConverge, configuration, preProcessingMillis, computeMillis, mutateMillis
Name  Type  Default  Optional  Description 

graphName 
String 

no 
The name of a graph stored in the catalog. 
configuration 
Map 

yes 
Configuration for algorithmspecifics and/or graph filtering. 
The configuration for the mutate
mode is similar to the write
mode.
Instead of specifying a writeProperty
, we need to specify a mutateProperty
.
Also, specifying writeConcurrency
is not possible in mutate
mode.
Name  Type  Description 

nodeCount 
Integer 
The number of nodes considered. 
ranIterations 
Integer 
The actual number of iterations the algorithm ran. 
didConverge 
Boolean 
An indicator of whether the algorithm found a correct coloring. 
colorCount 
Integer 
The number of colors used. 
preProcessingMillis 
Integer 
Milliseconds for preprocessing the data. 
computeMillis 
Integer 
Milliseconds for running the algorithm. 
mutateMillis 
Integer 
Milliseconds for adding properties to the projected graph. 
configuration 
Map 
The configuration used for running the algorithm. 
CALL gds.beta.k1coloring.write(graphName: String, configuration: Map)
YIELD nodeCount, colorCount, ranIterations, didConverge, configuration, preProcessingMillis, computeMillis, writeMillis
Name  Type  Default  Optional  Description 

graphName 
String 

no 
The name of a graph stored in the catalog. 
configuration 
Map 

yes 
Configuration for algorithmspecifics and/or graph filtering. 
Name  Type  Default  Optional  Description 

Integer 
4 
yes 
The number of concurrent threads used for running the algorithm. Also provides the default value for 'readConcurrency' and 'writeConcurrency'. This is dependent on the Neo4j edition; for more information, see CPU. 

Integer 
value of 'concurrency' 
yes 
The number of concurrent threads used for writing the result. 

Integer 
10 
yes 
The maximum number of iterations of K1 Coloring to run. 

String 
n/a 
no 
The node property this procedure writes the color to. 

minCommunitySize 
Integer 
0 
yes 
Only community ids of communities with a size greater than or equal to the given value are written to Neo4j. 
Name  Type  Description 

nodeCount 
Integer 
The number of nodes considered. 
ranIterations 
Integer 
The actual number of iterations the algorithm ran. 
didConverge 
Boolean 
An indicator of whether the algorithm found a correct coloring. 
colorCount 
Integer 
The number of colors used. 
preProcessingMillis 
Integer 
Milliseconds for preprocessing the data. 
computeMillis 
Integer 
Milliseconds for running the algorithm. 
writeMillis 
Integer 
Milliseconds for writing result data back to Neo4j. 
configuration 
Map 
The configuration used for running the algorithm. 
3. Examples
Consider the graph created by the following Cypher statement:
CREATE (alice:User {name: 'Alice'}),
(bridget:User {name: 'Bridget'}),
(charles:User {name: 'Charles'}),
(doug:User {name: 'Doug'}),
(alice)[:LINK]>(bridget),
(alice)[:LINK]>(charles),
(alice)[:LINK]>(doug),
(bridget)[:LINK]>(charles)
This graph has a super node with name "Alice" that connects to all other nodes. It should therefore not be possible for any other node to be assigned the same color as the Alice node.
CALL gds.graph.project(
'myGraph',
'User',
{
LINK : {
orientation: 'UNDIRECTED'
}
}
)
We can now go ahead and project a graph with all the User
nodes and the LINK
relationships with UNDIRECTED
orientation.
In the examples below we will use named graphs and native projections as the norm. However, Cypher projections can also be used. 
CALL gds.graph.project('myGraph', 'Person', 'LIKES')
In the following examples we will demonstrate using the K1 Coloring algorithm on this graph.
CALL gds.beta.k1coloring.stream('myGraph')
YIELD nodeId, color
RETURN gds.util.asNode(nodeId).name AS name, color
ORDER BY name
name  color 









It is also possible to write the assigned colors back to the database using the write
mode.
CALL gds.beta.k1coloring.write('myGraph', {writeProperty: 'color'})
YIELD nodeCount, colorCount, ranIterations, didConverge
nodeCount  colorCount  ranIterations  didConverge 





When using write
mode the procedure will return information about the algorithm execution.
In this example we return the number of processed nodes, the number of colors used to color the graph, the number of iterations and information whether the algorithm converged.
To instead mutate the inmemory graph with the assigned colors, the mutate
mode can be used as follows.
CALL gds.beta.k1coloring.mutate('myGraph', {mutateProperty: 'color'})
YIELD nodeCount, colorCount, ranIterations, didConverge
nodeCount  colorCount  ranIterations  didConverge 





Similar to the write
mode, stats
mode can run the algorithm and return only the execution statistics without persisting the results.
CALL gds.beta.k1coloring.stats('myGraph')
YIELD nodeCount, colorCount, ranIterations, didConverge
nodeCount  colorCount  ranIterations  didConverge 





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