This section describes the Triangle Count algorithm in the Neo4j Graph Data Science library.
This topic includes:
The Triangle Count algorithm counts the number of triangles for each node in the graph. A triangle is a set of three nodes where each node has a relationship to the other two. In graph theory terminology, this is sometimes referred to as a 3clique. The Triangle Count algorithm in the GDS library only finds triangles in undirected graphs.
Triangle counting has gained popularity in social network analysis, where it is used to detect communities and measure the cohesiveness of those communities. It can also be used to determine the stability of a graph, and is often used as part of the computation of network indices, such as clustering coefficients. The Triangle Count algorithm is also used to compute the Local Clustering Coefficient.
For more information on this algorithm, see:
This section covers the syntax used to execute the Triangle Count algorithm in each of its execution modes. The named graph variant of the syntax is described. To learn more about general syntax variants, see Section 5.1, “Syntax overview”.
The named graphs must be projected in the 
Run Triangle Count in stream mode on a named graph:
CALL gds.triangleCount.stream(
graphName: String,
configuration: Map
)
YIELD
nodeId: Integer,
triangleCount: Integer
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 

nodeLabels 
String[] 

yes 
Filter the named graph using the given node labels. 
relationshipTypes 
String[] 

yes 
Filter the named graph using the given relationship types. 
concurrency 
Integer 

yes 
The number of concurrent threads used for running the algorithm. 
Name  Type  Default  Optional  Description 

maxDegree 
Integer 

Yes 
If a node has a degree higher than this it will not be considered by the algorithm. The triangle count for these nodes will
be 
Name  Type  Description 

nodeId 
Integer 
Node ID. 
triangleCount 
Integer 
Number of triangles the node is part of. Is 
Run Triangle Count in stream mode on a named graph:
CALL gds.triangleCount.stats(
graphName: String,
configuration: Map
)
YIELD
triangleCount: Integer,
nodeCount: Integer,
createMillis: Integer,
computeMillis: 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 algorithmspecifics and/or graph filtering. 
Name  Type  Default  Optional  Description 

nodeLabels 
String[] 

yes 
Filter the named graph using the given node labels. 
relationshipTypes 
String[] 

yes 
Filter the named graph using the given relationship types. 
concurrency 
Integer 

yes 
The number of concurrent threads used for running the algorithm. 
Name  Type  Default  Optional  Description 

maxDegree 
Integer 

Yes 
If a node has a degree higher than this it will not be considered by the algorithm. The triangle count for these nodes will
be 
Name  Type  Description 

triangleCount 
Integer 
Total number of triangles in the graph. 
nodeCount 
Integer 
Number of nodes in the graph. 
createMillis 
Integer 
Milliseconds for creating the graph. 
computeMillis 
Integer 
Milliseconds for running the algorithm. 
configuration 
Map 
The configuration used for running the algorithm. 
Run Triangle Count in mutate mode on a named graph:
CALL gds.triangleCount.mutate(
graphName: String,
configuration: Map
)
YIELD
triangleCount: Integer,
nodeCount: Integer,
nodePropertiesWritten: Integer,
createMillis: Integer,
computeMillis: Integer,
mutateMillis: 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 algorithmspecifics and/or graph filtering. 
Name  Type  Default  Optional  Description 

nodeLabels 
String[] 

yes 
Filter the named graph using the given node labels. 
relationshipTypes 
String[] 

yes 
Filter the named graph using the given relationship types. 
concurrency 
Integer 

yes 
The number of concurrent threads used for running the algorithm. 
mutateProperty 
String 

no 
The node property in the GDS graph to which the triangle count is written. 
Name  Type  Default  Optional  Description 

maxDegree 
Integer 

Yes 
If a node has a degree higher than this it will not be considered by the algorithm. The triangle count for these nodes will
be 
Name  Type  Description 

triangleCount 
Integer 
Total number of triangles in the graph. 
nodeCount 
Integer 
Number of nodes in the graph. 
nodePropertiesWritten 
Integer 
Number of properties added to the inmemory graph. 
createMillis 
Integer 
Milliseconds for creating the graph. 
computeMillis 
Integer 
Milliseconds for running the algorithm. 
mutateMillis 
Integer 
Milliseconds for adding properties to the inmemory graph. 
configuration 
Map 
The configuration used for running the algorithm. 
Run Triangle Count in write mode on a named graph:
CALL gds.triangleCount.write(
graphName: String,
configuration: Map
)
YIELD
triangleCount: Integer,
nodeCount: Integer,
nodePropertiesWritten: Integer,
createMillis: Integer,
computeMillis: 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 algorithmspecifics and/or graph filtering. 
Name  Type  Default  Optional  Description 

nodeLabels 
String[] 

yes 
Filter the named graph using the given node labels. 
relationshipTypes 
String[] 

yes 
Filter the named graph using the given relationship types. 
concurrency 
Integer 

yes 
The number of concurrent threads used for running the algorithm. 
mutateProperty 
String 

no 
The node property in the GDS graph to which the triangle count is written. 
Name  Type  Default  Optional  Description 

maxDegree 
Integer 

Yes 
If a node has a degree higher than this it will not be considered by the algorithm. The triangle count for these nodes will
be 
Name  Type  Description 

triangleCount 
Integer 
Total number of triangles in the graph. 
nodeCount 
Integer 
Number of nodes in the graph. 
nodePropertiesWritten 
Integer 
Number of properties written to Neo4j. 
createMillis 
Integer 
Milliseconds for creating the graph. 
computeMillis 
Integer 
Milliseconds for running the algorithm. 
writeMillis 
Integer 
Milliseconds for writing results back to Neo4j. 
configuration 
Map 
The configuration used for running the algorithm. 
It is also possible to execute the algorithm on a graph that is projected in conjunction with the algorithm execution.
In this case, the graph does not have a name, and we call it anonymous.
When executing over an anonymous graph the configuration map contains a graph projection configuration as well as an algorithm
configuration.
All execution modes support execution on anonymous graphs, although we only show syntax and modespecific configuration for
the write
mode for brevity.
For more information on syntax variants, see Section 5.1, “Syntax overview”.
Run Triangle Count in write mode on an anonymous graph:
CALL gds.triangleCount.write(
configuration: Map
)
YIELD
triangleCount: Integer,
nodeCount: Integer,
nodePropertiesWritten: Integer,
createMillis: Integer,
computeMillis: Integer,
writeMillis: Integer,
configuration: Map
Name  Type  Default  Optional  Description 

nodeProjection 
String, String[] or Map 

yes 
The node projection used for anonymous graph creation via a Native projection. 
relationshipProjection 
String, String[] or Map 

yes 
The relationship projection used for anonymous graph creation a Native projection. 
nodeQuery 
String 

yes 
The Cypher query used to select the nodes for anonymous graph creation via a Cypher projection. 
relationshipQuery 
String 

yes 
The Cypher query used to select the relationships for anonymous graph creation via a Cypher projection. 
nodeProperties 
String, String[] or Map 

yes 
The node properties to project during anonymous graph creation. 
relationshipProperties 
String, String[] or Map 

yes 
The relationship properties to project during anonymous graph creation. 
concurrency 
Integer 

yes 
The number of concurrent threads used for running the algorithm. Also provides the default value for 'readConcurrency' and 'writeConcurrency'. 
readConcurrency 
Integer 

yes 
The number of concurrent threads used for creating the graph. 
writeConcurrency 
Integer 

yes 
The number of concurrent threads used for writing the result to Neo4j. 
writeProperty 
String 

no 
The node property in the Neo4j database to which the triangle count is written. 
Name  Type  Default  Optional  Description 

maxDegree 
Integer 

Yes 
If a node has a degree higher than this it will not be considered by the algorithm. The triangle count for these nodes will
be 
The results are the same as for running write mode with a named graph, specified above.
In addition to the standard execution modes there is an alpha
procedure gds.alpha.triangles
that can be used to list all triangles in the graph.
This algorithm is in the alpha tier. For more information on this tier of algorithm, see here.
The following will return a stream of node IDs for each triangle:
CALL gds.alpha.triangles(
graphName: String,
configuration: Map
)
YIELD nodeA, nodeB, nodeC
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 

nodeLabels 
String[] 

yes 
Filter the named graph using the given node labels. 
relationshipTypes 
String[] 

yes 
Filter the named graph using the given relationship types. 
concurrency 
Integer 

yes 
The number of concurrent threads used for running the algorithm. 
Name  Type  Description 

nodeA 
Integer 
The ID of the first node in the given triangle. 
nodeB 
Integer 
The ID of the second node in the given triangle. 
nodeC 
Integer 
The ID of the third node in the given triangle. 
In this section we will show examples of executing the Triangle Count 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 social network graph of a handful nodes connected in a particular pattern. The example graph looks like this:
The following Cypher statement will create the example graph in the Neo4j database:
CREATE
(alice:Person {name: 'Alice'}),
(michael:Person {name: 'Michael'}),
(karin:Person {name: 'Karin'}),
(chris:Person {name: 'Chris'}),
(will:Person {name: 'Will'}),
(mark:Person {name: 'Mark'}),
(michael)[:KNOWS]>(karin),
(michael)[:KNOWS]>(chris),
(will)[:KNOWS]>(michael),
(mark)[:KNOWS]>(michael),
(mark)[:KNOWS]>(will),
(alice)[:KNOWS]>(michael),
(will)[:KNOWS]>(chris),
(chris)[:KNOWS]>(karin)
With the graph in Neo4j we can now project it into the graph catalog to prepare it for algorithm execution.
We do this using a native projection targeting the Person
nodes and the KNOWS
relationships.
For the relationships we must use the UNDIRECTED
orientation.
This is because the Triangle Count algorithm is defined only for undirected graphs.
In the examples below we will use named graphs and native projections as the norm. However, anonymous graphs and/or Cypher projections can also be used. 
The following statement will create a graph using a native projection and store it in the graph catalog under the name 'myGraph'.
CALL gds.graph.create(
'myGraph',
'Person',
{
KNOWS: {
orientation: 'UNDIRECTED'
}
}
)
The Triangle Count algorithm requires the graph to be created using the 
In the following examples we will demonstrate using the Triangle Count algorithm on this graph.
First off, we will estimate the cost of running the algorithm using the estimate
procedure.
This can be done with any execution mode.
We will use the write
mode in this example.
Estimating the algorithm is useful to understand the memory impact that running the algorithm on your graph will have.
When you later actually run the algorithm in one of the execution modes the system will perform an estimation.
If the estimation shows that there is a very high probability of the execution going over its memory limitations, the execution
is prohibited.
To read more about this, see Section 3.1.3, “Automatic estimation and execution blocking”.
For more details on estimate
in general, see Section 3.1, “Memory Estimation”.
The following will estimate the memory requirements for running the algorithm in write mode:
CALL gds.triangleCount.write.estimate('myGraph', { writeProperty: 'triangleCount' })
YIELD nodeCount, relationshipCount, bytesMin, bytesMax, requiredMemory
nodeCount  relationshipCount  bytesMin  bytesMax  requiredMemory 

6 
16 
144 
144 
"144 Bytes" 
Note that the relationship count is 16 although we only created 8 relationships in the original Cypher statement.
This is because we used the UNDIRECTED
orientation, which will project each relationship in each direction, effectively doubling the number of relationships.
In the stream
execution mode, the algorithm returns the triangle count for each node.
This allows us to inspect the results directly or postprocess them in Cypher without any side effects.
For example, we can order the results to find the nodes with the highest triangle count.
For more details on the stream
mode in general, see Section 3.3.1, “Stream”.
The following will run the algorithm in stream
mode:
CALL gds.triangleCount.stream('myGraph')
YIELD nodeId, triangleCount
RETURN gds.util.asNode(nodeId).name AS name, triangleCount
ORDER BY triangleCount DESC
name  triangleCount 

"Michael" 
3 
"Chris" 
2 
"Will" 
2 
"Karin" 
1 
"Mark" 
1 
"Alice" 
0 
Here we find that the 'Michael' node has the most triangles.
This can be verified in the example graph.
Since the 'Alice' node only KNOWS
one other node, it can not be part of any triangle, and indeed the algorithm reports a count of zero.
In the stats
execution mode, the algorithm returns a single row containing a summary of the algorithm result.
The summary result contains the global triangle count, which is the total number of triangles in the entire graph.
This execution mode does not have any side effects.
It can be useful for evaluating algorithm performance by inspecting the computeMillis
return item.
In the examples below we will omit returning the timings.
The full signature of the procedure can be found in the syntax section.
For more details on the stats
mode in general, see Section 3.3.4, “Stats”.
The following will run the algorithm in stats
mode:
CALL gds.triangleCount.stats('myGraph')
YIELD globalTriangleCount, nodeCount
globalTriangleCount  nodeCount 

3 
6 
Here we can see that the graph has six nodes with a total number of three triangles. Comparing this to the stream example we can see that the 'Michael' node has a triangle count equal to the global triangle count. In other words, that node is part of all of the triangles in the graph and thus has a very central position in the graph.
The mutate
execution mode extends the stats
mode with an important side effect: updating the named graph with a new node property containing the triangle count for that
node.
The name of the new property is specified using the mandatory configuration parameter mutateProperty
.
The result is a single summary row, similar to stats
, but with some additional metrics.
The mutate
mode is especially useful when multiple algorithms are used in conjunction.
For example, using the triangle count to compute the local clustering coefficient.
For more details on the mutate
mode in general, see Section 3.3.3, “Mutate”.
The following will run the algorithm in mutate
mode:
CALL gds.triangleCount.mutate('myGraph', {
mutateProperty: 'triangles'
})
YIELD globalTriangleCount, nodeCount
globalTriangleCount  nodeCount 

3 
6 
The returned result is the same as in the stats
example.
Additionally, the graph 'myGraph' now has a node property triangles
which stores the triangle count for each node.
To find out how to inspect the new schema of the inmemory graph, see Section 4.1.2, “Listing graphs in the catalog”.
The write
execution mode extends the stats
mode with an important side effect: writing the triangle count for each node as a property to the Neo4j database.
The name of the new property is specified using the mandatory configuration parameter writeProperty
.
The result is a single summary row, similar to stats
, but with some additional metrics.
The write
mode enables directly persisting the results to the database.
For more details on the write
mode in general, see Section 3.3.2, “Write”.
The following will run the algorithm in write
mode:
CALL gds.triangleCount.write('myGraph', {
writeProperty: 'triangles'
})
YIELD globalTriangleCount, nodeCount
globalTriangleCount  nodeCount 

3 
6 
The returned result is the same as in the stats
example.
Additionally, each of the six nodes now has a new property triangles
in the Neo4j database, containing the triangle count for that node.
The Triangle Count algorithm supports a maxDegree
configuration parameter that can be used to exclude nodes from processing if their degree is greater than the configured
value.
This can be useful to speed up the computation when there are nodes with a very high degree (socalled super nodes) in the
graph.
Super nodes have a great impact on the performance of the Triangle Count algorithm.
To learn about the degree distribution of your graph, see Section 4.1.2, “Listing graphs in the catalog”.
The nodes excluded from the computation get assigned a triangle count of 1
.
The following will run the algorithm in stream
mode with the maxDegree
parameter:
CALL gds.triangleCount.stream('myGraph', {
maxDegree: 4
})
YIELD nodeId, triangleCount
RETURN gds.util.asNode(nodeId).name AS name, triangleCount
ORDER BY name ASC
name  triangleCount 

"Alice" 
0 
"Chris" 
0 
"Karin" 
0 
"Mark" 
0 
"Michael" 
1 
"Will" 
0 
Running the algorithm on the example graph with maxDegree: 4
excludes the 'Michael' node from the computation, as it has a degree of 5.
As this node is part of all the triangles in the example graph excluding it results in no triangles.
It is also possible to list all the triangles in the graph.
To do this we make use of the alpha
procedure gds.alpha.triangles
.
This algorithm is in the alpha tier. For more information on this tier of algorithm, see here.
The following will compute a stream of node IDs for each triangle and return the name property of the nodes:
CALL gds.alpha.triangles('myGraph')
YIELD nodeA, nodeB, nodeC
RETURN
gds.util.asNode(nodeA).name AS nodeA,
gds.util.asNode(nodeB).name AS nodeB,
gds.util.asNode(nodeC).name AS nodeC
nodeA  nodeB  nodeC 

"Michael" 
"Karin" 
"Chris" 
"Michael" 
"Chris" 
"Will" 
"Michael" 
"Will" 
"Mark" 
We can see that there are three triangles in the graph: "Will, Michael, and Chris", "Will, Mark, and Michael", and "Michael, Karin, and Chris". The node "Alice" is not part of any triangle and thus does not appear in the triangles listing.