# Louvain

## 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

The Louvain method is an algorithm to detect communities in large networks. It maximizes a modularity score for each community, where the modularity quantifies the quality of an assignment of nodes to communities. This means evaluating how much more densely connected the nodes within a community are, compared to how connected they would be in a random network.

The Louvain algorithm is a hierarchical clustering algorithm, that recursively merges communities into a single node and executes the modularity clustering on the condensed graphs.

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.

## Syntax

This section covers the syntax used to execute the Louvain 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.

Louvain syntax per mode
Run Louvain in stream mode on a named graph.
``````CALL gds.louvain.stream(
graphName: String,
configuration: Map
)
YIELD
nodeId: Integer,
communityId: Integer,
intermediateCommunityIds: List of 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. Configuration
Name Type Default Optional Description

nodeLabels

List of String

`['*']`

yes

Filter the named graph using the given node labels. Nodes with any of the given labels will be included.

relationshipTypes

List of String

`['*']`

yes

Filter the named graph using the given relationship types. Relationships with any of the given types will be included.

concurrency

Integer

`4`

yes

The number of concurrent threads used for running the algorithm.

jobId

String

`Generated internally`

yes

An ID that can be provided to more easily track the algorithm’s progress.

logProgress

Boolean

`true`

yes

If disabled the progress percentage will not be logged.

relationshipWeightProperty

String

`null`

yes

Name of the relationship property to use as weights. If unspecified, the algorithm runs unweighted.

seedProperty

String

`n/a`

yes

Used to set the initial community for a node. The property value needs to be a non-negative number.

maxLevels

Integer

`10`

yes

The maximum number of levels in which the graph is clustered and then condensed.

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.

includeIntermediateCommunities

Boolean

`false`

yes

Indicates whether to write intermediate communities. If set to false, only the final community is persisted.

consecutiveIds

Boolean

`false`

yes

Flag to decide whether component identifiers are mapped into a consecutive id space (requires additional memory). Cannot be used in combination with the `includeIntermediateCommunities` flag.

minCommunitySize

Integer

`0`

yes

Only nodes inside communities larger or equal the given value are returned.

Table 3. Results
Name Type Description

nodeId

Integer

Node ID.

communityId

Integer

The community ID of the final level.

intermediateCommunityIds

List of Integer

Community IDs for each level. `Null` if `includeIntermediateCommunities` is set to false.

Run Louvain in stats mode on a named graph.
``````CALL gds.louvain.stats(
graphName: String,
configuration: Map
)
YIELD
preProcessingMillis: Integer,
computeMillis: Integer,
postProcessingMillis: Integer,
communityCount: Integer,
ranLevels: Integer,
modularity: Float,
modularities: List of Float,
communityDistribution: Map,
configuration: Map``````
Table 4. 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 5. Configuration
Name Type Default Optional Description

nodeLabels

List of String

`['*']`

yes

Filter the named graph using the given node labels. Nodes with any of the given labels will be included.

relationshipTypes

List of String

`['*']`

yes

Filter the named graph using the given relationship types. Relationships with any of the given types will be included.

concurrency

Integer

`4`

yes

The number of concurrent threads used for running the algorithm.

jobId

String

`Generated internally`

yes

An ID that can be provided to more easily track the algorithm’s progress.

logProgress

Boolean

`true`

yes

If disabled the progress percentage will not be logged.

relationshipWeightProperty

String

`null`

yes

Name of the relationship property to use as weights. If unspecified, the algorithm runs unweighted.

seedProperty

String

`n/a`

yes

Used to set the initial community for a node. The property value needs to be a non-negative number.

maxLevels

Integer

`10`

yes

The maximum number of levels in which the graph is clustered and then condensed.

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.

includeIntermediateCommunities

Boolean

`false`

yes

Indicates whether to write intermediate communities. If set to false, only the final community is persisted.

consecutiveIds

Boolean

`false`

yes

Flag to decide whether component identifiers are mapped into a consecutive id space (requires additional memory). Cannot be used in combination with the `includeIntermediateCommunities` flag.

Table 6. Results
Name Type Description

preProcessingMillis

Integer

Milliseconds for preprocessing the data.

computeMillis

Integer

Milliseconds for running the algorithm.

postProcessingMillis

Integer

Milliseconds for computing percentiles and community count.

communityCount

Integer

The number of communities found.

ranLevels

Integer

The number of supersteps the algorithm actually ran.

modularity

Float

The final modularity score.

modularities

List of Float

The modularity scores for each level.

communityDistribution

Map

Map containing min, max, mean as well as p1, p5, p10, p25, p50, p75, p90, p95, p99 and p999 percentile values of community size for the last level.

configuration

Map

The configuration used for running the algorithm.

Run Louvain in mutate mode on a named graph.
``````CALL gds.louvain.mutate(
graphName: String,
configuration: Map
)
YIELD
preProcessingMillis: Integer,
computeMillis: Integer,
mutateMillis: Integer,
postProcessingMillis: Integer,
communityCount: Integer,
ranLevels: Integer,
modularity: Float,
modularities: List of Float,
nodePropertiesWritten: Integer,
communityDistribution: Map,
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. Configuration
Name Type Default Optional Description

mutateProperty

String

`n/a`

no

The node property in the GDS graph to which the community ID is written.

nodeLabels

List of String

`['*']`

yes

Filter the named graph using the given node labels.

relationshipTypes

List of String

`['*']`

yes

Filter the named graph using the given relationship types.

concurrency

Integer

`4`

yes

The number of concurrent threads used for running the algorithm.

jobId

String

`Generated internally`

yes

An ID that can be provided to more easily track the algorithm’s progress.

relationshipWeightProperty

String

`null`

yes

Name of the relationship property to use as weights. If unspecified, the algorithm runs unweighted.

seedProperty

String

`n/a`

yes

Used to set the initial community for a node. The property value needs to be a non-negative number.

maxLevels

Integer

`10`

yes

The maximum number of levels in which the graph is clustered and then condensed.

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.

includeIntermediateCommunities

Boolean

`false`

yes

Indicates whether to write intermediate communities. If set to false, only the final community is persisted.

consecutiveIds

Boolean

`false`

yes

Flag to decide whether component identifiers are mapped into a consecutive id space (requires additional memory). Cannot be used in combination with the `includeIntermediateCommunities` flag.

Table 9. 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.

communityCount

Integer

The number of communities found.

ranLevels

Integer

The number of supersteps the algorithm actually ran.

modularity

Float

The final modularity score.

modularities

List of Float

The modularity scores for each level.

nodePropertiesWritten

Integer

Number of properties added to the projected graph.

communityDistribution

Map

Map containing min, max, mean as well as p1, p5, p10, p25, p50, p75, p90, p95, p99 and p999 percentile values of community size for the last level.

configuration

Map

The configuration used for running the algorithm.

Run Louvain in write mode on a named graph.
``````CALL gds.louvain.write(
graphName: String,
configuration: Map
)
YIELD
preProcessingMillis: Integer,
computeMillis: Integer,
writeMillis: Integer,
postProcessingMillis: Integer,
nodePropertiesWritten: Integer,
communityCount: Integer,
ranLevels: Integer,
modularity: Float,
modularities: List of Float,
communityDistribution: Map,
configuration: Map``````
Table 10. 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 11. Configuration
Name Type Default Optional Description

nodeLabels

List of String

`['*']`

yes

Filter the named graph using the given node labels. Nodes with any of the given labels will be included.

relationshipTypes

List of String

`['*']`

yes

Filter the named graph using the given relationship types. Relationships with any of the given types will be included.

concurrency

Integer

`4`

yes

The number of concurrent threads used for running the algorithm.

jobId

String

`Generated internally`

yes

An ID that can be provided to more easily track the algorithm’s progress.

logProgress

Boolean

`true`

yes

If disabled the progress percentage will not be logged.

writeConcurrency

Integer

`value of 'concurrency'`

yes

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

writeProperty

String

`n/a`

no

The node property in the Neo4j database to which the community ID is written.

relationshipWeightProperty

String

`null`

yes

Name of the relationship property to use as weights. If unspecified, the algorithm runs unweighted.

seedProperty

String

`n/a`

yes

Used to set the initial community for a node. The property value needs to be a non-negative number.

maxLevels

Integer

`10`

yes

The maximum number of levels in which the graph is clustered and then condensed.

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.

includeIntermediateCommunities

Boolean

`false`

yes

Indicates whether to write intermediate communities. If set to false, only the final community is persisted.

consecutiveIds

Boolean

`false`

yes

Flag to decide whether component identifiers are mapped into a consecutive id space (requires additional memory). Cannot be used in combination with the `includeIntermediateCommunities` flag.

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 12. 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.

nodePropertiesWritten

Integer

The number of node properties written.

communityCount

Integer

The number of communities found.

ranLevels

Integer

The number of supersteps the algorithm actually ran.

modularity

Float

The final modularity score.

modularities

List of Float

The modularity scores for each level.

communityDistribution

Map

Map containing min, max, mean as well as p1, p5, p10, p25, p50, p75, p90, p95, p99 and p999 percentile values of community size for the last level.

configuration

Map

The configuration used for running the algorithm.

## Examples

 All the examples below should be run in an empty database. The examples use Cypher projections as the norm. Native projections will be deprecated in a future release.

In this section we will show examples of running the Louvain community detection 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
(nAlice:User {name: 'Alice', seed: 42}),
(nBridget:User {name: 'Bridget', seed: 42}),
(nCharles:User {name: 'Charles', seed: 42}),
(nDoug:User {name: 'Doug'}),
(nMark:User {name: 'Mark'}),
(nMichael:User {name: 'Michael'}),

(nAlice)-[:LINK {weight: 1}]->(nBridget),
(nAlice)-[:LINK {weight: 1}]->(nCharles),
(nCharles)-[:LINK {weight: 1}]->(nBridget),

(nAlice)-[:LINK {weight: 5}]->(nDoug),

(nMark)-[:LINK {weight: 1}]->(nDoug),
(nMark)-[:LINK {weight: 1}]->(nMichael),
(nMichael)-[:LINK {weight: 1}]->(nMark);``````

This graph has two clusters of Users, that are closely connected. Between those clusters there is one single edge. The relationships that connect the nodes in each component have a property `weight` which determines the strength of the relationship.

We can now project the graph and store it in the graph catalog. We load the `LINK` relationships with orientation set to `UNDIRECTED` as this works best with the Louvain algorithm.

The following statement will project the graph and store it in the graph catalog.
``````MATCH (source:User)
OPTIONAL MATCH (source)-[r:LINK]->(target:User)
RETURN gds.graph.project(
'myGraph',
source,
target,
{
sourceNodeProperties: source { .seed },
targetNodeProperties: target { .seed },
relationshipProperties: r { .weight }
},
{ undirectedRelationshipTypes: ['*'] }
)``````

In the following examples we will demonstrate using the Louvain algorithm on this graph.

### Memory Estimation

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 Automatic estimation and execution blocking.

For more details on `estimate` in general, see Memory Estimation.

The following will estimate the memory requirements for running the algorithm:
``````CALL gds.louvain.write.estimate('myGraph', { writeProperty: 'community' })
YIELD nodeCount, relationshipCount, bytesMin, bytesMax, requiredMemory``````
Table 13. Results
nodeCount relationshipCount bytesMin bytesMax requiredMemory

6

14

5321

563216

"[5321 Bytes ... 550 KiB]"

### Stream

In the `stream` execution mode, the algorithm returns the community ID for each node. This allows us to inspect the results directly or post-process them in Cypher without any side effects.

For more details on the `stream` mode in general, see Stream.

The following will run the algorithm and stream results:
``````CALL gds.louvain.stream('myGraph')
YIELD nodeId, communityId, intermediateCommunityIds
RETURN gds.util.asNode(nodeId).name AS name, communityId
ORDER BY name ASC``````
Table 14. Results
name communityId

"Alice"

3

"Bridget"

3

"Charles"

3

"Doug"

5

"Mark"

5

"Michael"

5

We use default values for the procedure configuration parameter. Levels and `innerIterations` are set to 10 and the tolerance value is 0.0001.

### Stats

In the `stats` execution mode, the algorithm returns a single row containing a summary of the algorithm result. 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 Stats.

The following will run the algorithm and returns the result in form of statistical and measurement values
``````CALL gds.louvain.stats('myGraph')
YIELD communityCount``````
Table 15. Results
communityCount

2

### Mutate

The `mutate` execution mode extends the `stats` mode with an important side effect: updating the named graph with a new node property containing the community ID 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 more details on the `mutate` mode in general, see Mutate.

The following will run the algorithm and store the results in `myGraph`:
``````CALL gds.louvain.mutate('myGraph', { mutateProperty: 'communityId' })
YIELD communityCount, modularity, modularities``````
Table 16. Results
communityCount modularity modularities

2

0.3571428571428571

[0.3571428571428571]

In `mutate` mode, only a single row is returned by the procedure. The result contains meta information, like the number of identified communities and the modularity values. In contrast to the `write` mode the result is written to the GDS in-memory graph instead of the Neo4j database.

### Write

The `write` execution mode extends the `stats` mode with an important side effect: writing the community ID 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 Write.

The following run the algorithm, and write back results:
``````CALL gds.louvain.write('myGraph', { writeProperty: 'community' })
YIELD communityCount, modularity, modularities``````
Table 17. Results
communityCount modularity modularities

2

0.3571428571428571

[0.3571428571428571]

When writing back the results, only a single row is returned by the procedure. The result contains meta information, like the number of identified communities and the modularity values.

### Weighted

The Louvain algorithm can also run on weighted graphs, taking the given relationship weights into concern when calculating the modularity.

The following will run the algorithm on a weighted graph and stream results:
``````CALL gds.louvain.stream('myGraph', { relationshipWeightProperty: 'weight' })
YIELD nodeId, communityId, intermediateCommunityIds
RETURN gds.util.asNode(nodeId).name AS name, communityId
ORDER BY name ASC``````
Table 18. Results
name communityId

"Alice"

1

"Bridget"

3

"Charles"

3

"Doug"

1

"Mark"

5

"Michael"

5

Using the weighted relationships, we see that `Alice` and `Doug` have formed their own community, as their link is much stronger than all the others.

### Seeded

The Louvain algorithm can be run incrementally, by providing a seed property. With the seed property an initial community mapping can be supplied for a subset of the loaded nodes. The algorithm will try to keep the seeded community IDs.

The following will run the algorithm and stream results:
``````CALL gds.louvain.stream('myGraph', { seedProperty: 'seed' })
YIELD nodeId, communityId, intermediateCommunityIds
RETURN gds.util.asNode(nodeId).name AS name, communityId
ORDER BY name ASC``````
Table 19. Results
name communityId

"Alice"

42

"Bridget"

42

"Charles"

42

"Doug"

47

"Mark"

47

"Michael"

47

Using the seeded graph, we see that the community around `Alice` keeps its initial community ID of `42`. The other community is assigned a new community ID, which is guaranteed to be larger than the largest seeded community ID. Note that the `consecutiveIds` configuration option cannot be used in combination with seeding in order to retain the seeding values.

### Using intermediate communities

As described before, Louvain is a hierarchical clustering algorithm. That means that after every clustering step all nodes that belong to the same cluster are reduced to a single node. Relationships between nodes of the same cluster become self-relationships, relationships to nodes of other clusters connect to the clusters representative. This condensed graph is then used to run the next level of clustering. The process is repeated until the clusters are stable.

In order to demonstrate this iterative behavior, we need to construct a more complex graph.

``````CREATE (a:Node {name: 'a'})
CREATE (b:Node {name: 'b'})
CREATE (c:Node {name: 'c'})
CREATE (d:Node {name: 'd'})
CREATE (e:Node {name: 'e'})
CREATE (f:Node {name: 'f'})
CREATE (g:Node {name: 'g'})
CREATE (h:Node {name: 'h'})
CREATE (i:Node {name: 'i'})
CREATE (j:Node {name: 'j'})
CREATE (k:Node {name: 'k'})
CREATE (l:Node {name: 'l'})
CREATE (m:Node {name: 'm'})
CREATE (n:Node {name: 'n'})
CREATE (x:Node {name: 'x'})

CREATE (a)-[:TYPE]->(b)
CREATE (a)-[:TYPE]->(d)
CREATE (a)-[:TYPE]->(f)
CREATE (b)-[:TYPE]->(d)
CREATE (b)-[:TYPE]->(x)
CREATE (b)-[:TYPE]->(g)
CREATE (b)-[:TYPE]->(e)
CREATE (c)-[:TYPE]->(x)
CREATE (c)-[:TYPE]->(f)
CREATE (d)-[:TYPE]->(k)
CREATE (e)-[:TYPE]->(x)
CREATE (e)-[:TYPE]->(f)
CREATE (e)-[:TYPE]->(h)
CREATE (f)-[:TYPE]->(g)
CREATE (g)-[:TYPE]->(h)
CREATE (h)-[:TYPE]->(i)
CREATE (h)-[:TYPE]->(j)
CREATE (i)-[:TYPE]->(k)
CREATE (j)-[:TYPE]->(k)
CREATE (j)-[:TYPE]->(m)
CREATE (j)-[:TYPE]->(n)
CREATE (k)-[:TYPE]->(m)
CREATE (k)-[:TYPE]->(l)
CREATE (l)-[:TYPE]->(n)
CREATE (m)-[:TYPE]->(n);``````
The following statement will project the graph and store it in the graph catalog.
``````MATCH (source:Node)
OPTIONAL MATCH (source)-[r:TYPE]->(target:Node)
RETURN gds.graph.project(
'myGraph2',
source,
target,
{},
{ undirectedRelationshipTypes: ['*'] }
)``````

#### Stream intermediate communities

The following run the algorithm and stream results including the intermediate communities:
``````CALL gds.louvain.stream('myGraph2', { includeIntermediateCommunities: true })
YIELD nodeId, communityId, intermediateCommunityIds
RETURN gds.util.asNode(nodeId).name AS name, communityId, intermediateCommunityIds
ORDER BY name ASC``````
Table 20. Results
name communityId intermediateCommunityIds

"a"

6

[2, 6]

"b"

6

[2, 6]

"c"

6

[6, 6]

"d"

6

[2, 6]

"e"

6

[6, 6]

"f"

6

[6, 6]

"g"

11

[11, 11]

"h"

11

[11, 11]

"i"

11

[11, 11]

"j"

10

[10, 10]

"k"

10

[10, 10]

"l"

10

[10, 10]

"m"

10

[10, 10]

"n"

10

[10, 10]

"x"

6

[6, 6]

In this example graph, after the first iteration we see 4 clusters, which in the second iteration are reduced to three.

#### Mutate intermediate communities

The following run the algorithm and mutate the in-memory graph:
``````CALL gds.louvain.mutate('myGraph2', {
mutateProperty: 'intermediateCommunities',
includeIntermediateCommunities: true
})
YIELD communityCount, modularity, modularities``````
Table 21. Results
communityCount modularity modularities

3

0.3816

[0.37599999999999995, 0.3816]

The following stream the mutated property from the in-memory graph:
``````CALL gds.graph.nodeProperty.stream('myGraph2', 'intermediateCommunities')
YIELD nodeId, propertyValue
RETURN
gds.util.asNode(nodeId).name AS name,
toIntegerList(propertyValue) AS intermediateCommunities
ORDER BY name ASC``````
Table 22. Results
name intermediateCommunities

"a"

[2, 6]

"b"

[2, 6]

"c"

[6, 6]

"d"

[2, 6]

"e"

[6, 6]

"f"

[6, 6]

"g"

[11, 11]

"h"

[11, 11]

"i"

[11, 11]

"j"

[10, 10]

"k"

[10, 10]

"l"

[10, 10]

"m"

[10, 10]

"n"

[10, 10]

"x"

[6, 6]

#### Write intermediate communities

The following run the algorithm and write to the Neo4j database:
``````CALL gds.louvain.write('myGraph2', {
writeProperty: 'intermediateCommunities',
includeIntermediateCommunities: true
})
YIELD communityCount, modularity, modularities``````
Table 23. Results
communityCount modularity modularities

3

0.3816

[0.37599999999999995, 0.3816]

The following stream the written property from the Neo4j database:
``````MATCH (n:Node) RETURN n.name AS name, toIntegerList(n.intermediateCommunities) AS intermediateCommunities
ORDER BY name ASC``````
Table 24. Results
name intermediateCommunities

"a"

[2, 6]

"b"

[2, 6]

"c"

[6, 6]

"d"

[2, 6]

"e"

[6, 6]

"f"

[6, 6]

"g"

[11, 11]

"h"

[11, 11]

"i"

[11, 11]

"j"

[10, 10]

"k"

[10, 10]

"l"

[10, 10]

"m"

[10, 10]

"n"

[10, 10]

"x"

[6, 6]