Modularity Optimization

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

Supported algorithm traits:

Directed

Undirected

Heterogeneous nodes

Heterogeneous relationships

Weighted relationships

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.

1. Introduction

The Modularity Optimization algorithm tries to detect communities in the graph based on their modularity. Modularity is a measure of the structure of a graph, measuring the density of connections within a module or community. Graphs with a high modularity score will have many connections within a community but only few pointing outwards to other communities. The algorithm will explore for every node if its modularity score might increase if it changes its community to one of its neighboring nodes.

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

Modularity Optimization syntax per mode

Run Modularity Optimization in stream mode on a named graph.

CALL gds.beta.modularityOptimization.stream(graphName: String, configuration: Map)
YIELD
  nodeId: Integer,
  communityId: Integer

Table 1. Parameters
Name	Type	Default	Optional	Description
graphName	String	`n/a`	no	The name of a graph stored in the catalog.
configuration	Map	`{}`	yes	Configuration for algorithm-specifics and/or graph filtering.

Table 2. General configuration
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'.
writeConcurrency	Integer	`value of 'concurrency'`	yes	The number of concurrent threads used for writing the result (applicable in WRITE mode).
logProgress	Boolean	`true`	yes	If disabled the progress percentage will not be logged.

Table 3. Algorithm specific configuration
Name	Type	Default	Optional	Description
maxIterations	Integer	10	yes	The maximum number of iterations to run.
tolerance	Float	0.0001	yes	Minimum change in modularity between iterations. If the modularity changes less than the tolerance value, the result is considered stable and the algorithm returns.
seedProperty	String	n/a	yes	Used to define initial set of labels (must be a non-negative number).
consecutiveIds	Boolean	false	yes	Flag to decide whether component identifiers are mapped into a consecutive id space (requires additional memory).
relationshipWeightProperty	String	null	yes	Name of the relationship property to use as weights. If unspecified, the algorithm runs unweighted.
minCommunitySize	Integer	0	yes	Only nodes inside communities larger or equal the given value are returned.

Table 4. Results
Name	Type	Description
nodeId	Integer	Node ID
communityId	Integer	Community ID

Run Modularity Optimization in mutate mode on a named graph.

CALL gds.beta.modularityOptimization.mutate(graphName: String, configuration: Map})
YIELD
  preProcessingMillis: Integer,
  computeMillis: Integer,
  postProcessingMillis: Integer,
  mutateMillis: Integer,
  communityCount: Integer,
  communityDistribution: Map,
  modularity: Float,
  ranIterations: Integer,
  didConverge: Boolean,
  nodes: Integer,
  configuration: Map

Table 5. Parameters
Name	Type	Default	Optional	Description
graphName	String	`n/a`	no	The name of a graph stored in the catalog.
configuration	Map	`{}`	yes	Configuration for algorithm-specifics 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.

Table 6. Results
Name	Type	Description
preProcessingMillis	Integer	Milliseconds for preprocessing the data.
computeMillis	Integer	Milliseconds for running the algorithm.
mutateMillis	Integer	Milliseconds for adding properties to the projected graph.
postProcessingMillis	Integer	Milliseconds for computing percentiles and community count.
nodes	Integer	The number of nodes considered.
didConverge	Boolean	True if the algorithm did converge to a stable modularity score within the provided number of maximum iterations.
ranIterations	Integer	The number of iterations run.
modularity	Float	The final modularity score.
communityCount	Integer	The number of communities found.
communityDistribution	Map	The containing min, max, mean as well as 50, 75, 90, 95, 99 and 999 percentile of community size.
configuration	Map	The configuration used for running the algorithm.

Run Modularity Optimization in write mode on a named graph.

CALL gds.beta.modularityOptimization.write(graphName: String, configuration: Map})
YIELD
  preProcessingMillis: Integer,
  computeMillis: Integer,
  postProcessingMillis: Integer,
  writeMillis: Integer,
  communityCount: Integer,
  communityDistribution: Map,
  modularity: Float,
  ranIterations: Integer,
  didConverge: Boolean,
  nodes: Integer,
  configuration: Map

Table 7. Parameters
Name	Type	Default	Optional	Description
graphName	String	`n/a`	no	The name of a graph stored in the catalog.
configuration	Map	`{}`	yes	Configuration for algorithm-specifics and/or graph filtering.

Table 8. General configuration
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'.
writeConcurrency	Integer	`value of 'concurrency'`	yes	The number of concurrent threads used for writing the result (applicable in WRITE mode).
logProgress	Boolean	`true`	yes	If disabled the progress percentage will not be logged.

Table 9. Algorithm specific configuration
Name	Type	Default	Optional	Description
seedProperty	String	`n/a`	yes	Used to set the initial community for a node. The property value needs to be a number.
writeProperty	String	`n/a`	yes	The property name written back the ID of the partition particular node belongs to.
maxIterations	Integer	`10`	yes	The maximum number of iterations that the modularity optimization will run for each level.
tolerance	Float	`0.0001`	yes	Minimum change in modularity between iterations. If the modularity changes less than the tolerance value, the result is considered stable and the algorithm returns.
consecutiveIds	Boolean	`false`	yes	Flag to decide whether component identifiers are mapped into a consecutive id space (requires additional memory).
relationshipWeightProperty	String	`null`	yes	Name of the relationship property to use as weights. If unspecified, the algorithm runs unweighted.
minCommunitySize	Integer	`0`	yes	Only community ids of communities with a size greater than or equal to the given value are written to Neo4j.

Table 10. Results
Name	Type	Description
preProcessingMillis	Integer	Milliseconds for preprocessing the data.
computeMillis	Integer	Milliseconds for running the algorithm.
writeMillis	Integer	Milliseconds for writing result data back.
postProcessingMillis	Integer	Milliseconds for computing percentiles and community count.
nodes	Integer	The number of nodes considered.
didConverge	Boolean	True if the algorithm did converge to a stable modularity score within the provided number of maximum iterations.
ranIterations	Integer	The number of iterations run.
modularity	Float	The final modularity score.
communityCount	Integer	The number of communities found.
communityDistribution	Map	The containing min, max, mean as well as 50, 75, 90, 95, 99 and 999 percentile of community size.
configuration	Map	The configuration used for running the algorithm.

3. Examples

Consider the graph created by the following Cypher statement:

CREATE
  (a:Person {name:'Alice'})
, (b:Person {name:'Bridget'})
, (c:Person {name:'Charles'})
, (d:Person {name:'Doug'})
, (e:Person {name:'Elton'})
, (f:Person {name:'Frank'})
, (a)-[:KNOWS {weight: 0.01}]->(b)
, (a)-[:KNOWS {weight: 5.0}]->(e)
, (a)-[:KNOWS {weight: 5.0}]->(f)
, (b)-[:KNOWS {weight: 5.0}]->(c)
, (b)-[:KNOWS {weight: 5.0}]->(d)
, (c)-[:KNOWS {weight: 0.01}]->(e)
, (f)-[:KNOWS {weight: 0.01}]->(d)

This graph consists of two center nodes "Alice" and "Bridget" each of which have two more neighbors. Additionally, each neighbor of "Alice" is connected to one of the neighbors of "Bridget". Looking at the weights of the relationships, it can be seen that the connections from the two center nodes to their neighbors are very strong, while connections between those groups are weak. Therefore the Modularity Optimization algorithm should detect two communities: "Alice" and "Bob" together with their neighbors respectively.

In the examples below we will use named graphs and native projections as the norm. However, Cypher projections can also be used.

The following statement will project the graph and store it in the graph catalog.

CALL gds.graph.project(
    'myGraph',
    'Person',
    {
        KNOWS: {
            orientation: 'UNDIRECTED',
            properties: ['weight']
        }
    })

The following example demonstrates using the Modularity Algorithm on this weighted graph.

Running the Modularity Optimization algorithm in stream mode:

CALL gds.beta.modularityOptimization.stream('myGraph', { relationshipWeightProperty: 'weight' })
YIELD nodeId, communityId
RETURN gds.util.asNode(nodeId).name AS name, communityId
ORDER BY name

Table 11. Results
name	communityId
"Alice"	4
"Bridget"	1
"Charles"	1
"Doug"	1
"Elton"	4
"Frank"	4

It is also possible to write the assigned community ids back to the database using the write mode.

Running the Modularity Optimization algorithm in write mode:

CALL gds.beta.modularityOptimization.write('myGraph', { relationshipWeightProperty: 'weight', writeProperty: 'community' })
YIELD nodes, communityCount, ranIterations, didConverge

Table 12. Results
nodes	communityCount	ranIterations	didConverge
6	2	2	true

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 communities assigned to the nodes in the graph, the number of iterations and information whether the algorithm converged.

Running the algorithm without specifying the relationshipWeightProperty will default all relationship weights to 1.0.

To instead mutate the in-memory graph with the assigned community ids, the mutate mode is used.

Running the Modularity Optimization algorithm in mutate mode:

CALL gds.beta.modularityOptimization.mutate('myGraph', { relationshipWeightProperty: 'weight', mutateProperty: 'community' })
YIELD nodes, communityCount, ranIterations, didConverge

Table 13. Results
nodes	communityCount	ranIterations	didConverge
6	2	2	true

When using mutate mode the procedure will return information about the algorithm execution as in write mode.

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