Speaker-Listener Label Propagation

The following content is a preview of an upcoming release. For documentation covering current releases, see https://neo4j.com/docs.

This section describes the Speaker-Listener Label Propagation algorithm in the Neo4j Graph Data Science library.

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

Stream mode
Stats mode
Mutate mode
Write 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:

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.

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