Filtered Node Similarity

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 Filtered Node Similarity algorithm is an extension to the Node Similarity algorithm. It adds support for filtering on source nodes, target nodes, or both.

Node filtering

A node filter reduces the node space for which the algorithm will produce results. Consider two similarity results: A = (alice)-[:SIMILAR_TO]→(bob) and B (bob)-[:SIMILAR_TO]→(alice). Result A will be produced if the (alice) node matches the source node filter and the (bob) node matches the target node filter If the (alice) node does not match the target node filter, or the (bob) node does not match the source node filter, result B will not be produce.

Configuring node filters

For the standard configuration of node similarity, see Node Similarity syntax.

The source node filter is specified with the sourceNodeFilter configuration parameter. The target node filter is specified with the targetNodeFilter configuration parameter. Neither parameter is mandatory.

The node filter parameters accept one of the following:

Table 1. Syntax for `sourceNodeFilter` and `targetNodeFilter`
a single node id	`sourceNodeFilter: 42`
a list of node ids	`sourceNodeFilter: [23, 42, 87]`
a single node	`MATCH (person:Person) WITH person ORDER BY person.age DESC LIMIT 1` … `sourceNodeFilter: n`
a list of nodes	`MATCH (person:Person) WHERE person.age > 35 collect(person) AS people` … `sourceNodeFilter: people`
a single label	`sourceNodeFilter: 'Person'`

Syntax

This section covers the syntax used to execute the Filtered Node Similarity 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.

Filtered Node Similarity syntax per mode

Run Filtered Node Similarity in stream mode on a named graph.

CALL gds.nodeSimilarity.filtered.stream(
  graphName: String,
  configuration: Map
) YIELD
  node1: Integer,
  node2: Integer,
  similarity: Float

Table 2. 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 3. 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.
similarityCutoff	Float	`1E-42`	yes	Lower limit for the similarity score to be present in the result. Values must be between 0 and 1.
degreeCutoff	Integer	`1`	yes	Inclusive lower bound on the node degree for a node to be considered in the comparisons. This value can not be lower than 1.
upperDegreeCutoff	Integer	`2147483647`	yes	Inclusive upper bound on the node degree for a node to be considered in the comparisons. This value can not be lower than 1.
topK	Integer	`10`	yes	Limit on the number of scores per node. The K largest results are returned. This value cannot be lower than 1.
bottomK	Integer	`10`	yes	Limit on the number of scores per node. The K smallest results are returned. This value cannot be lower than 1.
topN	Integer	`0`	yes	Global limit on the number of scores computed. The N largest total results are returned. This value cannot be negative, a value of 0 means no global limit.
bottomN	Integer	`0`	yes	Global limit on the number of scores computed. The N smallest total results are returned. This value cannot be negative, a value of 0 means no global limit.
relationshipWeightProperty	String	`null`	yes	Name of the relationship property to use as weights. If unspecified, the algorithm runs unweighted.
similarityMetric	String	`JACCARD`	yes	The metric used to compute similarity. Can be either `JACCARD`, `OVERLAP` or `COSINE`.
useComponents	Boolean or String	`false`	yes	If enabled, Node Similarity will use components to improve the performance of the computation, skipping comparisons of nodes in different components. Set to `false` (Default): the algorithm does not use components, but computes similarity across the entire graph. Set to `true`: the algorithm uses components, and will compute these components before computing similarity. Set to String: use pre-computed components stored in the graph, with String as the key for a node property representing components
sourceNodeFilter	Integer or List of Integer or String	`n/a`	no	The source node filter to apply. Accepts a single node id, a List of node ids, or a single label.
targetNodeFilter	Integer or List of Integer or String	`n/a`	no	The target node filter to apply. Accepts a single node id, a List of node ids, or a single label.

Table 4. Results
Name	Type	Description
`node1`	Integer	Node ID of the first node.
`node2`	Integer	Node ID of the second node.
`similarity`	Float	Similarity score for the two nodes.

Run Node Similarity in stats mode on a named graph.

CALL gds.nodeSimilarity.filtered.stats(
  graphName: String,
  configuration: Map
)
YIELD
  preProcessingMillis: Integer,
  computeMillis: Integer,
  postProcessingMillis: Integer,
  nodesCompared: Integer,
  similarityPairs: Integer,
  similarityDistribution: Map,
  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.

Table 6. 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.
similarityCutoff	Float	`1E-42`	yes	Lower limit for the similarity score to be present in the result. Values must be between 0 and 1.
degreeCutoff	Integer	`1`	yes	Inclusive lower bound on the node degree for a node to be considered in the comparisons. This value can not be lower than 1.
upperDegreeCutoff	Integer	`2147483647`	yes	Inclusive upper bound on the node degree for a node to be considered in the comparisons. This value can not be lower than 1.
topK	Integer	`10`	yes	Limit on the number of scores per node. The K largest results are returned. This value cannot be lower than 1.
bottomK	Integer	`10`	yes	Limit on the number of scores per node. The K smallest results are returned. This value cannot be lower than 1.
topN	Integer	`0`	yes	Global limit on the number of scores computed. The N largest total results are returned. This value cannot be negative, a value of 0 means no global limit.
bottomN	Integer	`0`	yes	Global limit on the number of scores computed. The N smallest total results are returned. This value cannot be negative, a value of 0 means no global limit.
relationshipWeightProperty	String	`null`	yes	Name of the relationship property to use as weights. If unspecified, the algorithm runs unweighted.
similarityMetric	String	`JACCARD`	yes	The metric used to compute similarity. Can be either `JACCARD`, `OVERLAP` or `COSINE`.
useComponents	Boolean or String	`false`	yes	If enabled, Node Similarity will use components to improve the performance of the computation, skipping comparisons of nodes in different components. Set to `false` (Default): the algorithm does not use components, but computes similarity across the entire graph. Set to `true`: the algorithm uses components, and will compute these components before computing similarity. Set to String: use pre-computed components stored in the graph, with String as the key for a node property representing components
sourceNodeFilter	Integer or List of Integer or String	`n/a`	no	The source node filter to apply. Accepts a single node id, a List of node ids, or a single label.
targetNodeFilter	Integer or List of Integer or String	`n/a`	no	The target node filter to apply. Accepts a single node id, a List of node ids, or a single label.

Table 7. Results
Name	Type	Description
preProcessingMillis	Integer	Milliseconds for preprocessing the data.
computeMillis	Integer	Milliseconds for running the algorithm.
postProcessingMillis	Integer	Milliseconds for computing component count and distribution statistics.
nodesCompared	Integer	The number of nodes for which similarity was computed.
similarityPairs	Integer	The number of similarities in the result.
similarityDistribution	Map	Map containing min, max, mean as well as p50, p75, p90, p95, p99 and p999 percentile values of the computed similarity results.
configuration	Map	The configuration used for running the algorithm.

Run Filtered Node Similarity in mutate mode on a named graph.

CALL gds.nodeSimilarity.filtered.mutate(
  graphName: String,
  configuration: Map
)
YIELD
  preProcessingMillis: Integer,
  computeMillis: Integer,
  mutateMillis: Integer,
  postProcessingMillis: Integer,
  relationshipsWritten: Integer,
  nodesCompared: Integer,
  similarityDistribution: Map,
  configuration: Map

Table 8. 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 9. Configuration
Name	Type	Default	Optional	Description
mutateRelationshipType	String	`n/a`	no	The relationship type used for the new relationships written to the projected graph.
mutateProperty	String	`n/a`	no	The relationship property in the GDS graph to which the similarity score 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.
similarityCutoff	Float	`1E-42`	yes	Lower limit for the similarity score to be present in the result. Values must be between 0 and 1.
degreeCutoff	Integer	`1`	yes	Inclusive lower bound on the node degree for a node to be considered in the comparisons. This value can not be lower than 1.
upperDegreeCutoff	Integer	`2147483647`	yes	Inclusive upper bound on the node degree for a node to be considered in the comparisons. This value can not be lower than 1.
topK	Integer	`10`	yes	Limit on the number of scores per node. The K largest results are returned. This value cannot be lower than 1.
bottomK	Integer	`10`	yes	Limit on the number of scores per node. The K smallest results are returned. This value cannot be lower than 1.
topN	Integer	`0`	yes	Global limit on the number of scores computed. The N largest total results are returned. This value cannot be negative, a value of 0 means no global limit.
bottomN	Integer	`0`	yes	Global limit on the number of scores computed. The N smallest total results are returned. This value cannot be negative, a value of 0 means no global limit.
relationshipWeightProperty	String	`null`	yes	Name of the relationship property to use as weights. If unspecified, the algorithm runs unweighted.
similarityMetric	String	`JACCARD`	yes	The metric used to compute similarity. Can be either `JACCARD`, `OVERLAP` or `COSINE`.
useComponents	Boolean or String	`false`	yes	If enabled, Node Similarity will use components to improve the performance of the computation, skipping comparisons of nodes in different components. Set to `false` (Default): the algorithm does not use components, but computes similarity across the entire graph. Set to `true`: the algorithm uses components, and will compute these components before computing similarity. Set to String: use pre-computed components stored in the graph, with String as the key for a node property representing components
sourceNodeFilter	Integer or List of Integer or String	`n/a`	no	The source node filter to apply. Accepts a single node id, a List of node ids, or a single label.
targetNodeFilter	Integer or List of Integer or String	`n/a`	no	The target node filter to apply. Accepts a single node id, a List of node ids, or a single label.

Table 10. 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.
`nodesCompared`	Integer	The number of nodes for which similarity was computed.
`relationshipsWritten`	Integer	The number of relationships created.
`similarityDistribution`	Map	Map containing min, max, mean, stdDev and p1, p5, p10, p25, p75, p90, p95, p99, p100 percentile values of the computed similarity results.
`configuration`	Map	The configuration used for running the algorithm.

Run Filtered Node Similarity in write mode on a named graph.

CALL gds.nodeSimilarity.filtered.write(
  graphName: String,
  configuration: Map
)
YIELD
 preProcessingMillis: Integer,
  computeMillis: Integer,
  writeMillis: Integer,
  postProcessingMillis: Integer,
  nodesCompared: Integer,
  relationshipsWritten: Integer,
  similarityDistribution: Map,
  configuration: Map

Table 11. 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 12. 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.
writeRelationshipType	String	`n/a`	no	The relationship type used to persist the computed relationships in the Neo4j database.
writeProperty	String	`n/a`	no	The relationship property in the Neo4j database to which the similarity score is written.
similarityCutoff	Float	`1E-42`	yes	Lower limit for the similarity score to be present in the result. Values must be between 0 and 1.
degreeCutoff	Integer	`1`	yes	Inclusive lower bound on the node degree for a node to be considered in the comparisons. This value can not be lower than 1.
upperDegreeCutoff	Integer	`2147483647`	yes	Inclusive upper bound on the node degree for a node to be considered in the comparisons. This value can not be lower than 1.
topK	Integer	`10`	yes	Limit on the number of scores per node. The K largest results are returned. This value cannot be lower than 1.
bottomK	Integer	`10`	yes	Limit on the number of scores per node. The K smallest results are returned. This value cannot be lower than 1.
topN	Integer	`0`	yes	Global limit on the number of scores computed. The N largest total results are returned. This value cannot be negative, a value of 0 means no global limit.
bottomN	Integer	`0`	yes	Global limit on the number of scores computed. The N smallest total results are returned. This value cannot be negative, a value of 0 means no global limit.
relationshipWeightProperty	String	`null`	yes	Name of the relationship property to use as weights. If unspecified, the algorithm runs unweighted.
similarityMetric	String	`JACCARD`	yes	The metric used to compute similarity. Can be either `JACCARD`, `OVERLAP` or `COSINE`.
useComponents	Boolean or String	`false`	yes	If enabled, Node Similarity will use components to improve the performance of the computation, skipping comparisons of nodes in different components. Set to `false` (Default): the algorithm does not use components, but computes similarity across the entire graph. Set to `true`: the algorithm uses components, and will compute these components before computing similarity. Set to String: use pre-computed components stored in the graph, with String as the key for a node property representing components
sourceNodeFilter	Integer or List of Integer or String	`n/a`	no	The source node filter to apply. Accepts a single node id, a List of node ids, or a single label.
targetNodeFilter	Integer or List of Integer or String	`n/a`	no	The target node filter to apply. Accepts a single node id, a List of node ids, or a single label.

Table 13. Results
Name	Type	Description
preProcessingMillis	Integer	Milliseconds for preprocessing data.
computeMillis	Integer	Milliseconds for running the algorithm.
writeMillis	Integer	Milliseconds for writing result data back to Neo4j.
postProcessingMillis	Integer	Milliseconds for computing percentiles.
nodesCompared	Integer	The number of nodes for which similarity was computed.
relationshipsWritten	Integer	The number of relationships created.
similarityDistribution	Map	Map containing min, max, mean, stdDev and p1, p5, p10, p25, p75, p90, p95, p99, p100 percentile values of the computed similarity results.
configuration	Map	The configuration used for running the algorithm.

Examples

All the examples below should be run in an empty database.

The examples use native projections as the norm, although Cypher projections can be used as well.

In this section we will show examples of running the Filtered Node Similarity 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 knowledge 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:Singer {name: 'Alice'}),
  (bob:Person:Singer {name: 'Bob'}),
  (carol:Person:Singer {name: 'Carol'}),
  (dave:Person {name: 'Dave'}),
  (eve:Person:Singer {name: 'Eve'}),
  (guitar:Instrument {name: 'Guitar'}),
  (synth:Instrument {name: 'Synthesizer'}),
  (bongos:Instrument {name: 'Bongos'}),
  (trumpet:Instrument {name: 'Trumpet'}),

  (alice)-[:LIKES]->(guitar),
  (alice)-[:LIKES]->(synth),
  (alice)-[:LIKES {strength: 0.5}]->(bongos),
  (bob)-[:LIKES]->(guitar),
  (bob)-[:LIKES]->(synth),
  (carol)-[:LIKES]->(bongos),
  (dave)-[:LIKES]->(guitar),
  (dave)-[:LIKES]->(synth),
  (dave)-[:LIKES]->(bongos);

This bipartite graph has two node sets, Person nodes and Instrument nodes. Some of the Person nodes are also singers. The two node sets are connected via LIKES relationships. Each relationship starts at a Person node and ends at an Instrument node.

The Filtered Node Similarity algorithm will only compute similarity for nodes that have a degree of at least 1. Eve hence shall not be included in the results as her degree is zero.

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

CALL gds.graph.project(
    'myGraph',
    ['Person', 'Instrument','Singer'],
    {
        LIKES: {
            properties: {
                strength: {
                    property: 'strength',
                    defaultValue: 1.0
                }
            }
        }
    }
);

In the following examples we will demonstrate the usage of the Filtered Node Similarity algorithm on this graph. In particular, we will apply the sourceNodeFilter and targetNodeFilter filters to limit our similarity search to strictly Person nodes that also have the Singer label.

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.nodeSimilarity.filtered.write.estimate('myGraph', {
    writeRelationshipType: 'SIMILAR',
    writeProperty: 'score',
    sourceNodeFilter:'Singer',
    targetNodeFilter:'Singer'
})
YIELD nodeCount, relationshipCount, bytesMin, bytesMax, requiredMemory

Table 14. Results
nodeCount	relationshipCount	bytesMin	bytesMax	requiredMemory
9	9	2384	2600	"[2384 Bytes ... 2600 Bytes]"

Stream

In the stream execution mode, the algorithm returns the similarity score for each relationship. 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 the results:

CALL gds.nodeSimilarity.filtered.stream('myGraph', {sourceNodeFilter:'Singer' , targetNodeFilter:'Singer' } )
YIELD node1, node2, similarity
RETURN gds.util.asNode(node1).name AS Person1, gds.util.asNode(node2).name AS Person2, similarity
ORDER BY similarity DESCENDING, Person1, Person2

Table 15. Results
Person1	Person2	similarity
"Alice"	"Bob"	0.6666666666666666
"Bob"	"Alice"	0.6666666666666666
"Alice"	"Carol"	0.3333333333333333
"Carol"	"Alice"	0.3333333333333333

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 Node Similarity algorithm with the specified filters sand return the result in form of statistical and measurement values

CALL gds.nodeSimilarity.filtered.stats('myGraph', {sourceNodeFilter:'Singer' , targetNodeFilter:'Singer' } )
YIELD nodesCompared, similarityPairs

Table 16. Results
nodesCompared	similarityPairs
3	4

Mutate

The mutate execution mode extends the stats mode with an important side effect: updating the named graph with a new relationship property containing the similarity score for that relationship. 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 write back results to the in-memory graph:

CALL gds.nodeSimilarity.filtered.mutate('myGraph',{
    mutateRelationshipType: 'SIMILAR',
    mutateProperty: 'score',
    sourceNodeFilter:'Singer',
    targetNodeFilter:'Singer'
})

YIELD nodesCompared, relationshipsWritten

Table 17. Results
nodesCompared	relationshipsWritten
3	4

As can be seen in the results, the number of created relationships is the same as the number of rows in the streaming example.

The relationships that are produced by the mutation are always directed, even if the input graph is undirected. If a → b is topK for a and symmetrically b → a is topK for b (or both a → b and b → a are topN), it appears as though an undirected relationship is produced. However, they are just two directed relationships that have been independently produced.

Write

The write execution mode for each pair of nodes creates a relationship with their similarity score as a property to the Neo4j database. The type of the new relationship is specified using the mandatory configuration parameter writeRelationshipType. 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.

For more details on the write mode in general, see Write.

The following will run the algorithm, and write back results:

CALL gds.nodeSimilarity.filtered.write('myGraph',{
    writeRelationshipType: 'SIMILAR',
    writeProperty: 'score',
    sourceNodeFilter:'Singer',
    targetNodeFilter:'Singer'
})
YIELD nodesCompared, relationshipsWritten

Table 18. Results
nodesCompared	relationshipsWritten
3	4

As we can see from the results, the number of created relationships is equal to the number of rows in the streaming example.

The relationships that are written are always directed, even if the input graph is undirected. If a → b is topK for a and symmetrically b → a is topK for b (or both a → b and b → a are topN), it appears as though an undirected relationship is written. However, they are just two directed relationships that have been independently written.