Speaker-Listener Label Propagation

This algorithm is in the alpha tier. For more information on algorithm tiers, see Graph algorithms.

1. Introduction

The Speaker-Listener Label Propagation Algorithm (SLLPA) is a variation of the Label Propagation algorithm that is able to detect multiple communities per node. The GDS implementation is based on the SLPA: Uncovering Overlapping Communities in Social Networks via A Speaker-listener Interaction Dynamic Process publication by Xie et al.

The algorithm is randomized in nature and will not produce deterministic results. To accommodate this, we recommend using a higher number of iterations.

2. Syntax

This section covers the syntax used to execute the SLLPA 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.

SLLPA syntax per mode
Run SLLPA in stream mode on a named graph.
CALL gds.alpha.sllpa.stream(
  graphName: String,
  configuration: Map
)
YIELD
  nodeId: Integer,
  values: Map {
    communtiyIds: 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.

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.

maxIterations

Integer

n/a

no

Maximum number of iterations to run.

minAssociationStrength

String

0.2

yes

Minimum influence required for a community to retain a node.

Table 3. Results
Name Type Description

nodeId

Integer

Node ID.

values

Map

A map that contains the key communityIds.

Run SLLPA in stats mode on a named graph.
CALL gds.alpha.sllpa.stats(
  graphName: String,
  configuration: Map
)
YIELD
  ranIterations: Integer,
  didConverge: Boolean,
  preProcessingMillis: Integer,
  computeMillis: Integer,
  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.

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.

maxIterations

Integer

n/a

no

Maximum number of iterations to run.

minAssociationStrength

String

0.2

yes

Minimum influence required for a community to retain a node.

Table 6. Results
Name Type Description

ranIterations

Integer

Number of iterations run.

didConverge

Boolean

Indicates if the algorithm converged.

preProcessingMillis

Integer

Milliseconds for preprocessing the graph.

computeMillis

Integer

Milliseconds for running the algorithm.

configuration

Map

Configuration used for running the algorithm.

Run SLLPA in mutate mode on a named graph.
CALL gds.alpha.sllpa.mutate(
  graphName: String,
  configuration: Map
)
YIELD
  ranIterations: Integer,
  didConverge: Boolean,
  preProcessingMillis: Integer,
  computeMillis: Integer,
  mutateMillis: Integer,
  nodePropertiesWritten: 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. Configuration
Name Type Default Optional Description

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.

mutateProperty

String

""

yes

The prefix used for all public properties in the PregelSchema.

jobId

String

Generated internally

yes

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

maxIterations

Integer

n/a

no

Maximum number of iterations to run.

minAssociationStrength

String

0.2

yes

Minimum influence required for a community to retain a node.

Table 9. Results
Name Type Description

ranIterations

Integer

The number of iterations run.

didConverge

Boolean

Indicates if the algorithm converged.

preProcessingMillis

Integer

Milliseconds for preprocessing the graph.

computeMillis

Integer

Milliseconds for running the algorithm.

mutateMillis

Integer

Milliseconds for adding properties to the projected graph.

nodePropertiesWritten

Integer

The number of properties that were written to Neo4j.

configuration

Map

The configuration used for running the algorithm.

Run SLLPA in write mode on a named graph.
CALL gds.alpha.sllpa.write(
  graphName: String,
  configuration: Map
)
YIELD
  ranIterations: Integer,
  didConverge: Boolean,
  preProcessingMillis: Integer,
  computeMillis: Integer,
  writeMillis: Integer,
  nodePropertiesWritten: Integer,
  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.

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.

writeConcurrency

Integer

value of 'concurrency'

yes

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

writeProperty

String

""

yes

The prefix used for all public properties in the PregelSchema.

maxIterations

Integer

n/a

no

Maximum number of iterations to run.

minAssociationStrength

String

0.2

yes

Minimum influence required for a community to retain a node.

Table 12. Results
Name Type Description

ranIterations

Integer

The number of iterations run.

didConverge

Boolean

Indicates if the algorithm converged.

preProcessingMillis

Integer

Milliseconds for preprocessing the graph.

computeMillis

Integer

Milliseconds for running the algorithm.

writeMillis

Integer

Milliseconds for writing result data back.

nodePropertiesWritten

Integer

The number of properties that were written to Neo4j.

configuration

Map

The configuration used for running the algorithm.

3. Examples

In this section we will show examples of running the SLLPA 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:

Visualization of the example graph
The following Cypher statement will create the example graph in the Neo4j database:
CREATE
  (a:Person {name: 'Alice'}),
  (b:Person {name: 'Bob'}),
  (c:Person {name: 'Carol'}),
  (d:Person {name: 'Dave'}),
  (e:Person {name: 'Eve'}),
  (f:Person {name: 'Fredrick'}),
  (g:Person {name: 'Gary'}),
  (h:Person {name: 'Hilda'}),
  (i:Person {name: 'Ichabod'}),
  (j:Person {name: 'James'}),
  (k:Person {name: 'Khalid'}),

  (a)-[:KNOWS]->(b),
  (a)-[:KNOWS]->(c),
  (a)-[:KNOWS]->(d),
  (b)-[:KNOWS]->(c),
  (b)-[:KNOWS]->(d),
  (c)-[:KNOWS]->(d),

  (b)-[:KNOWS]->(e),
  (e)-[:KNOWS]->(f),
  (f)-[:KNOWS]->(g),
  (g)-[:KNOWS]->(h),

  (h)-[:KNOWS]->(i),
  (h)-[:KNOWS]->(j),
  (h)-[:KNOWS]->(k),
  (i)-[:KNOWS]->(j),
  (i)-[:KNOWS]->(k),
  (j)-[:KNOWS]->(k);

In the example, we will use the SLLPA algorithm to find the communities in the graph.

The following statement will project the graph and store it in the graph catalog.
CALL gds.graph.project(
  'myGraph',
  'Person',
  {
    KNOWS: {
      orientation: 'UNDIRECTED'
    }
  }
);

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

3.1. Stream

In the stream execution mode, the algorithm returns the community IDs 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.alpha.sllpa.stream('myGraph', {maxIterations: 100, minAssociationStrength: 0.1})
YIELD nodeId, values
RETURN gds.util.asNode(nodeId).name AS Name, values.communityIds AS communityIds
  ORDER BY Name ASC
Table 13. Results
Name communityIds

"Alice"

[0]

"Bob"

[0]

"Carol"

[0]

"Dave"

[0]

"Eve"

[0, 1]

"Fredrick"

[0, 1]

"Gary"

[0, 1]

"Hilda"

[1]

"Ichabod"

[1]

"James"

[1]

"Khalid"

[1]

Due to the randomness of the algorithm, the results will tend to vary between runs.