Filtered Node Similarity
Glossary
 Directed

Directed trait. The algorithm is welldefined 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 welldefined 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 Filtered Node Similarity algorithm is an extension to the Node Similarity algorithm. It adds support for filtering on source nodes, target nodes, or both.
2. 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.
3. 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:
a single node id 

a list of node ids 

a single node 

a list of nodes 

a single label 

4. 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.
CALL gds.nodeSimilarity.filtered.stream(
graphName: String,
configuration: Map
) YIELD
node1: Integer,
node2: Integer,
similarity: Float
Name  Type  Default  Optional  Description 

graphName 
String 

no 
The name of a graph stored in the catalog. 
configuration 
Map 

yes 
Configuration for algorithmspecifics and/or graph filtering. 
Name  Type  Default  Optional  Description 

List of String 

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

List of String 

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

Integer 

yes 
The number of concurrent threads used for running the algorithm. 

String 

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

Boolean 

yes 
If disabled the progress percentage will not be logged. 

similarityCutoff 
Float 

yes 
Lower limit for the similarity score to be present in the result. Values must be between 0 and 1. 
degreeCutoff 
Integer 

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 

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 

yes 
Limit on the number of scores per node. The K largest results are returned. This value cannot be lower than 1. 
bottomK 
Integer 

yes 
Limit on the number of scores per node. The K smallest results are returned. This value cannot be lower than 1. 
topN 
Integer 

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 

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. 
String 

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

similarityMetric 
String 

yes 
The metric used to compute similarity.
Can be either 
sourceNodeFilter 
Integer or List of Integer or String 

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 

no 
The target node filter to apply. Accepts a single node id, a List of node ids, or a single label. 
Name  Type  Description 


Integer 
Node ID of the first node. 

Integer 
Node ID of the second node. 

Float 
Similarity score for the two nodes. 
CALL gds.nodeSimilarity.filtered.stats(
graphName: String,
configuration: Map
)
YIELD
preProcessingMillis: Integer,
computeMillis: Integer,
postProcessingMillis: Integer,
nodesCompared: Integer,
similarityPairs: Integer,
similarityDistribution: Map,
configuration: Map
Name  Type  Default  Optional  Description 

graphName 
String 

no 
The name of a graph stored in the catalog. 
configuration 
Map 

yes 
Configuration for algorithmspecifics and/or graph filtering. 
Name  Type  Default  Optional  Description 

List of String 

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

List of String 

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

Integer 

yes 
The number of concurrent threads used for running the algorithm. 

String 

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

Boolean 

yes 
If disabled the progress percentage will not be logged. 

similarityCutoff 
Float 

yes 
Lower limit for the similarity score to be present in the result. Values must be between 0 and 1. 
degreeCutoff 
Integer 

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 

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 

yes 
Limit on the number of scores per node. The K largest results are returned. This value cannot be lower than 1. 
bottomK 
Integer 

yes 
Limit on the number of scores per node. The K smallest results are returned. This value cannot be lower than 1. 
topN 
Integer 

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 

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. 
String 

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

similarityMetric 
String 

yes 
The metric used to compute similarity.
Can be either 
sourceNodeFilter 
Integer or List of Integer or String 

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 

no 
The target node filter to apply. Accepts a single node id, a List of node ids, or a single label. 
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. 
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
Name  Type  Default  Optional  Description 

graphName 
String 

no 
The name of a graph stored in the catalog. 
configuration 
Map 

yes 
Configuration for algorithmspecifics and/or graph filtering. 
Name  Type  Default  Optional  Description 

mutateRelationshipType 
String 

no 
The relationship type used for the new relationships written to the projected graph. 
mutateProperty 
String 

no 
The relationship property in the GDS graph to which the similarity score is written. 
List of String 

yes 
Filter the named graph using the given node labels. 

List of String 

yes 
Filter the named graph using the given relationship types. 

Integer 

yes 
The number of concurrent threads used for running the algorithm. 

String 

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

similarityCutoff 
Float 

yes 
Lower limit for the similarity score to be present in the result. Values must be between 0 and 1. 
degreeCutoff 
Integer 

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 

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 

yes 
Limit on the number of scores per node. The K largest results are returned. This value cannot be lower than 1. 
bottomK 
Integer 

yes 
Limit on the number of scores per node. The K smallest results are returned. This value cannot be lower than 1. 
topN 
Integer 

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 

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. 
String 

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

similarityMetric 
String 

yes 
The metric used to compute similarity.
Can be either 
sourceNodeFilter 
Integer or List of Integer or String 

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 

no 
The target node filter to apply. Accepts a single node id, a List of node ids, or a single label. 
Name  Type  Description 


Integer 
Milliseconds for preprocessing the data. 

Integer 
Milliseconds for running the algorithm. 

Integer 
Milliseconds for adding properties to the projected graph. 

Integer 
Milliseconds for computing percentiles. 

Integer 
The number of nodes for which similarity was computed. 

Integer 
The number of relationships created. 

Map 
Map containing min, max, mean, stdDev and p1, p5, p10, p25, p75, p90, p95, p99, p100 percentile values of the computed similarity results. 

Map 
The configuration used for running the algorithm. 
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
Name  Type  Default  Optional  Description 

graphName 
String 

no 
The name of a graph stored in the catalog. 
configuration 
Map 

yes 
Configuration for algorithmspecifics and/or graph filtering. 
Name  Type  Default  Optional  Description 

List of String 

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

List of String 

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

Integer 

yes 
The number of concurrent threads used for running the algorithm. 

String 

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

Boolean 

yes 
If disabled the progress percentage will not be logged. 

Integer 

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

writeRelationshipType 
String 

no 
The relationship type used to persist the computed relationships in the Neo4j database. 
String 

no 
The relationship property in the Neo4j database to which the similarity score is written. 

similarityCutoff 
Float 

yes 
Lower limit for the similarity score to be present in the result. Values must be between 0 and 1. 
degreeCutoff 
Integer 

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 

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 

yes 
Limit on the number of scores per node. The K largest results are returned. This value cannot be lower than 1. 
bottomK 
Integer 

yes 
Limit on the number of scores per node. The K smallest results are returned. This value cannot be lower than 1. 
topN 
Integer 

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 

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. 
String 

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

similarityMetric 
String 

yes 
The metric used to compute similarity.
Can be either 
sourceNodeFilter 
Integer or List of Integer or String 

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 

no 
The target node filter to apply. Accepts a single node id, a List of node ids, or a single label. 
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. 
5. Examples
All the examples below should be run in an empty database. The examples use named graphs and 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:
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.
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.
5.1. 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.
CALL gds.nodeSimilarity.filtered.write.estimate('myGraph', {
writeRelationshipType: 'SIMILAR',
writeProperty: 'score',
sourceNodeFilter:'Singer',
targetNodeFilter:'Singer'
})
YIELD nodeCount, relationshipCount, bytesMin, bytesMax, requiredMemory
nodeCount  relationshipCount  bytesMin  bytesMax  requiredMemory 

9 
9 
2384 
2600 
"[2384 Bytes ... 2600 Bytes]" 
5.2. Stream
In the stream
execution mode, the algorithm returns the similarity score for each relationship.
This allows us to inspect the results directly or postprocess them in Cypher without any side effects.
For more details on the stream
mode in general, see Stream.
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
Person1  Person2  similarity 

"Alice" 
"Bob" 
0.6666666666666666 
"Bob" 
"Alice" 
0.6666666666666666 
"Alice" 
"Carol" 
0.3333333333333333 
"Carol" 
"Alice" 
0.3333333333333333 
5.3. 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.
CALL gds.nodeSimilarity.filtered.stats('myGraph', {sourceNodeFilter:'Singer' , targetNodeFilter:'Singer' } )
YIELD nodesCompared, similarityPairs
nodesCompared  similarityPairs 

3 
4 
5.4. 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.
CALL gds.nodeSimilarity.filtered.mutate('myGraph',{
mutateRelationshipType: 'SIMILAR',
mutateProperty: 'score',
sourceNodeFilter:'Singer',
targetNodeFilter:'Singer'
})
YIELD nodesCompared, relationshipsWritten
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 
5.5. 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.
CALL gds.nodeSimilarity.filtered.write('myGraph',{
writeRelationshipType: 'SIMILAR',
writeProperty: 'score',
sourceNodeFilter:'Singer',
targetNodeFilter:'Singer'
})
YIELD nodesCompared, relationshipsWritten
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 