This section describes the Node Similarity algorithm in the Neo4j Graph Data Science library. The algorithm is based on the Jaccard Similarity score.
This topic includes:
The Node Similarity algorithm compares a set of nodes based on the nodes they are connected to. Two nodes are considered similar if they share many of the same neighbors. Node Similarity computes pairwise similarities based on the Jaccard metric, also known as the Jaccard Similarity Score.
Jaccard Similarity is computed using the following formula:
The input of this algorithm is a bipartite, connected graph containing two disjoint node sets. Each relationship starts from a node in the first node set and ends at a node in the second node set.
The Node Similarity algorithm compares each node that has outgoing relationships with each other such node.
For every node n
, we collect the outgoing neighborhood N(n)
of that node, that is, all nodes m
such that there is a relationship from n
to m
.
For each pair n
, m
, the algorithm computes a similarity for that pair which is the Jaccard similarity of N(n)
and N(m)
.
The complexity of this comparison grows quadratically with the number of nodes to compare. The algorithm reduces the complexity by ignoring disconnected nodes.
In addition to computational complexity, the memory requirement for producing results also scales roughly quadratically. In order to bound memory usage, the algorithm requires an explicit limit on the number of results to compute per node. This is the 'topK' parameter. It can be set to any value, except 0.
The output of the algorithm are new relationships between pairs of the first node set. Similarity scores are expressed via relationship properties.
A related function for computing Jaccard similarity is described in Section 6.4.3, “Jaccard Similarity”.
For more information on this algorithm, see:
Running this algorithm requires sufficient available memory. Before running this algorithm, we recommend that you read Section 3.1, “Memory Estimation”. 
This section covers the syntax used to execute the 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 Section 6.1, “Syntax overview”.
Run Node Similarity in stream mode on a named graph.
CALL gds.nodeSimilarity.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 

nodeLabels 
String[] 

yes 
Filter the named graph using the given node labels. 
String[] 

yes 
Filter the named graph using the given relationship types. 

Integer 

yes 
The number of concurrent threads used for running the algorithm. 
Name  Type  Default  Optional  Description 

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 
Lower limit 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 
If set, the values stored at the given property are used as relationship weights during the computation. If not set, the graph is considered unweighted. 
Name  Type  Description 


Integer 
Node ID of the first node. 

Integer 
Node ID of the second node. 

Float 
Similarity score for the two nodes. 
Run Node Similarity in stats mode on a named graph.
CALL gds.nodeSimilarity.stats(
graphName: String,
configuration: Map
)
YIELD
createMillis: 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 

nodeLabels 
String[] 

yes 
Filter the named graph using the given node labels. 
String[] 

yes 
Filter the named graph using the given relationship types. 

Integer 

yes 
The number of concurrent threads used for running the algorithm. 
Name  Type  Default  Optional  Description 

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 
Lower limit 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 
If set, the values stored at the given property are used as relationship weights during the computation. If not set, the graph is considered unweighted. 
Name  Type  Description 

createMillis 
Integer 
Milliseconds for loading data. 
computeMillis 
Integer 
Milliseconds for running the algorithm. 
postProcessingMillis 
Integer 
Milliseconds for computing component count and distribution statistics. 
similarityPairs 
Integer 
The number of pairs of similar nodes computed. 
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 Node Similarity in mutate mode on a graph stored in the catalog.
CALL gds.nodeSimilarity.mutate(
graphName: String,
configuration: Map
)
YIELD
createMillis: 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 

nodeLabels 
String[] 

yes 
Filter the named graph using the given node labels. 
String[] 

yes 
Filter the named graph using the given relationship types. 

Integer 

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

mutateRelationshipType 
String 

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

no 
The relationship property in the GDS graph to which the similarity score is written. 
Name  Type  Default  Optional  Description 

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 
Lower limit 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 
If set, the values stored at the given property are used as relationship weights during the computation. If not set, the graph is considered unweighted. 
Name  Type  Description 

nodesCompared 
Integer 
The number of nodes compared. 
relationshipsWritten 
Integer 
The number of relationships created. 
createMillis 
Integer 
Milliseconds for loading data. 
computeMillis 
Integer 
Milliseconds for running the algorithm. 
mutateMillis 
Integer 
Milliseconds for adding properties to the inmemory graph. 
postProcessingMillis 
Integer 
Milliseconds for computing percentiles. 
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 Node Similarity in write mode on a graph stored in the catalog.
CALL gds.nodeSimilarity.write(
graphName: String,
configuration: Map
)
YIELD
createMillis: 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 

nodeLabels 
String[] 

yes 
Filter the named graph using the given node labels. 
String[] 

yes 
Filter the named graph using the given relationship types. 

Integer 

yes 
The number of concurrent threads used for running the algorithm. Also provides the default value for 'writeConcurrency'. 

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. 
Name  Type  Default  Optional  Description 

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 
Lower limit 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 
If set, the values stored at the given property are used as relationship weights during the computation. If not set, the graph is considered unweighted. 
Name  Type  Description 

nodesCompared 
Integer 
The number of nodes compared. 
relationshipsWritten 
Integer 
The number of relationships created. 
createMillis 
Integer 
Milliseconds for loading data. 
computeMillis 
Integer 
Milliseconds for running the algorithm. 
writeMillis 
Integer 
Milliseconds for writing result data back to Neo4j. 
postProcessingMillis 
Integer 
Milliseconds for computing percentiles. 
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. 
It is also possible to execute the algorithm on a graph that is projected in conjunction with the algorithm execution.
In this case, the graph does not have a name, and we call it anonymous.
When executing over an anonymous graph the configuration map contains a graph projection configuration as well as an algorithm
configuration.
All execution modes support execution on anonymous graphs, although we only show syntax and modespecific configuration for
the write
mode for brevity.
For more information on syntax variants, see Section 6.1, “Syntax overview”.
Run Node Similarity in write mode on an anonymous graph.
CALL gds.nodeSimilarity.write(
configuration: Map
)
YIELD
createMillis: Integer,
computeMillis: Integer,
writeMillis: Integer,
postProcessingMillis: Integer,
nodesCompared: Integer,
relationshipsWritten: Integer,
similarityDistribution: Map,
configuration: Map
Name  Type  Default  Optional  Description 

nodeProjection 
String, String[] or Map 

yes 
The node projection used for anonymous graph creation via a Native projection. 
relationshipProjection 
String, String[] or Map 

yes 
The relationship projection used for anonymous graph creation a Native projection. 
nodeQuery 
String 

yes 
The Cypher query used to select the nodes for anonymous graph creation via a Cypher projection. 
relationshipQuery 
String 

yes 
The Cypher query used to select the relationships for anonymous graph creation via a Cypher projection. 
nodeProperties 
String, String[] or Map 

yes 
The node properties to project during anonymous graph creation. 
relationshipProperties 
String, String[] or Map 

yes 
The relationship properties to project during anonymous graph creation. 
Integer 

yes 
The number of concurrent threads used for running the algorithm. Also provides the default value for 'readConcurrency' and 'writeConcurrency'. 

readConcurrency 
Integer 

yes 
The number of concurrent threads used for creating the graph. 
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. 
Name  Type  Default  Optional  Description 

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 
Lower limit 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 
If set, the values stored at the given property are used as relationship weights during the computation. If not set, the graph is considered unweighted. 
The results are the same as for running write mode with a named graph, see the write mode syntax above.
In this section we will show examples of running the 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 {name: 'Alice'}),
(bob:Person {name: 'Bob'}),
(carol:Person {name: 'Carol'}),
(dave:Person {name: 'Dave'}),
(eve:Person {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. The two node sets are connected via LIKES relationships. Each relationship starts at a Person node and ends at an Instrument node.
In the example, we want to use the Node Similarity algorithm to compare people based on the instruments they like.
The Node Similarity algorithm will only compute similarity for nodes that have a degree of at least 1. In the example graph, the Eve node will not be compared to other Person nodes.
In the examples below we will use named graphs and standard projections as the norm. However, Cypher projection and anonymous graphs could also be used. 
The following statement will create the graph and store it in the graph catalog.
CALL gds.graph.create(
'myGraph',
['Person', 'Instrument'],
{
LIKES: {
type: 'LIKES',
properties: {
strength: {
property: 'strength',
defaultValue: 1.0
}
}
}
}
);
In the following examples we will demonstrate using the Node Similarity algorithm on this graph.
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 Section 3.1.3, “Automatic estimation and execution blocking”.
For more details on estimate
in general, see Section 3.1, “Memory Estimation”.
The following will estimate the memory requirements for running the algorithm:
CALL gds.nodeSimilarity.write.estimate('myGraph', {
writeRelationshipType: 'SIMILAR',
writeProperty: 'score'
})
YIELD nodeCount, relationshipCount, bytesMin, bytesMax, requiredMemory
nodeCount  relationshipCount  bytesMin  bytesMax  requiredMemory 

9 
9 
2592 
2808 
"[2592 Bytes ... 2808 Bytes]" 
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 example, we can order the results to find the nodes with the highest local clustering coefficient.
For more details on the stream
mode in general, see Section 3.3.1, “Stream”.
The following will run the algorithm, and stream results:
CALL gds.nodeSimilarity.stream('myGraph')
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" 
"Dave" 
1.0 
"Dave" 
"Alice" 
1.0 
"Alice" 
"Bob" 
0.6666666666666666 
"Bob" 
"Alice" 
0.6666666666666666 
"Bob" 
"Dave" 
0.6666666666666666 
"Dave" 
"Bob" 
0.6666666666666666 
"Alice" 
"Carol" 
0.3333333333333333 
"Carol" 
"Alice" 
0.3333333333333333 
"Carol" 
"Dave" 
0.3333333333333333 
"Dave" 
"Carol" 
0.3333333333333333 
10 rows 
We use default values for the procedure configuration parameter. TopK is set to 10, topN is set to 0. Because of that the result set contains the top 10 similarity scores for each node.
If we would like to instead compare the Instruments to each other, we would then project the 
In the stats
execution mode, the algorithm returns a single row containing a summary of the algorithm result.
The summary result contains the avearage clustering coefficient of the graph, which is the normalised sum over all local clustering
coefficients.
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 Section 3.3.2, “Stats”.
The following will run the algorithm and returns the result in form of statistical and measurement values.
CALL gds.nodeSimilarity.stats('myGraph')
YIELD nodesCompared, similarityPairs
nodesCompared  similarityPairs 

4 
10 
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 example, using the triangle count to compute the local clustering coefficient.
For more details on the mutate
mode in general, see Section 3.3.3, “Mutate”.
The following will run the algorithm, and write back results:
CALL gds.nodeSimilarity.mutate('myGraph', {
mutateRelationshipType: 'SIMILAR',
mutateProperty: 'score'
})
YIELD nodesCompared, relationshipsWritten
nodesCompared  relationshipsWritten 

4 
10 
As we can see from the results, the number of created relationships is equal to the number of rows in the streaming example.
The write
execution mode extends the stats
mode with an important side effect: for each pair of nodes we create a relationship with the Jaccard 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 Section 3.3.4, “Write”.
The following will run the algorithm, and write back results:
CALL gds.nodeSimilarity.write('myGraph', {
writeRelationshipType: 'SIMILAR',
writeProperty: 'score'
})
YIELD nodesCompared, relationshipsWritten
nodesCompared  relationshipsWritten 

4 
10 
As we can see from the results, the number of created relationships is equal to the number of rows in the streaming example.
There are four limits that can be applied to the similarity results. Top limits the result to the highest similarity scores. Bottom limits the result to the lowest similarity scores. Both top and bottom limits can apply to the result as a whole ("N"), or to the result per node ("K").
There must always be a "K" limit, either bottomK or topK, which is a positive number. The default value for topK and bottomK is 10. 
total results  results per node  

highest score 
topN 
topK 
lowest score 
bottomN 
bottomK 
TopK and bottomK are limits on the number of scores computed per node. For topK, the K largest similarity scores per node are returned. For bottomK, the K smallest similarity scores per node are returned. TopK and bottomK cannot be 0, used in conjunction, and the default value is 10. If neither is specified, topK is used.
The following will run the algorithm, and stream the top 1 result per node:
CALL gds.nodeSimilarity.stream('myGraph', { topK: 1 })
YIELD node1, node2, similarity
RETURN gds.util.asNode(node1).name AS Person1, gds.util.asNode(node2).name AS Person2, similarity
ORDER BY Person1
Person1  Person2  similarity 

"Alice" 
"Dave" 
1.0 
"Bob" 
"Alice" 
0.6666666666666666 
"Carol" 
"Alice" 
0.3333333333333333 
"Dave" 
"Alice" 
1.0 
4 rows 
The following will run the algorithm, and stream the bottom 1 result per node:
CALL gds.nodeSimilarity.stream('myGraph', { bottomK: 1 })
YIELD node1, node2, similarity
RETURN gds.util.asNode(node1).name AS Person1, gds.util.asNode(node2).name AS Person2, similarity
ORDER BY Person1
Person1  Person2  similarity 

"Alice" 
"Carol" 
0.3333333333333333 
"Bob" 
"Alice" 
0.6666666666666666 
"Carol" 
"Alice" 
0.3333333333333333 
"Dave" 
"Carol" 
0.3333333333333333 
4 rows 
TopN and bottomN limit the number of similarity scores across all nodes. This is a limit on the total result set, in addition to the topK or bottomK limit on the results per node. For topN, the N largest similarity scores are returned. For bottomN, the N smallest similarity scores are returned. A value of 0 means no global limit is imposed and all results from topK or bottomK are returned.
The following will run the algorithm, and stream the 3 highest out of the top 1 results per node:
CALL gds.nodeSimilarity.stream('myGraph', { topK: 1, topN: 3 })
YIELD node1, node2, similarity
RETURN gds.util.asNode(node1).name AS Person1, gds.util.asNode(node2).name AS Person2, similarity
ORDER BY similarity DESC, Person1, Person2
Person1  Person2  similarity 

"Alice" 
"Dave" 
1.0 
"Dave" 
"Alice" 
1.0 
"Bob" 
"Alice" 
0.6666666666666666 
3 rows 
Degree cutoff is a lower limit on the node degree for a node to be considered in the comparisons. This value can not be lower than 1.
The following will ignore nodes with less than 3 LIKES relationships:
CALL gds.nodeSimilarity.stream('myGraph', { degreeCutoff: 3 })
YIELD node1, node2, similarity
RETURN gds.util.asNode(node1).name AS Person1, gds.util.asNode(node2).name AS Person2, similarity
ORDER BY Person1
Person1  Person2  similarity 

"Alice" 
"Dave" 
1.0 
"Dave" 
"Alice" 
1.0 
2 rows 
Similarity cutoff is a lower limit for the similarity score to be present in the result.
The default value is very small (1E42
) to exclude results with a similarity score of 0.
Setting similarity cutoff to 0 may yield a very large result set, increased runtime and memory consumption. 
The following will ignore node pairs with a similarity score less than 0.5:
CALL gds.nodeSimilarity.stream('myGraph', { similarityCutoff: 0.5 })
YIELD node1, node2, similarity
RETURN gds.util.asNode(node1).name AS Person1, gds.util.asNode(node2).name AS Person2, similarity
ORDER BY Person1
Person1  Person2  similarity 

"Alice" 
"Dave" 
1.0 
"Alice" 
"Bob" 
0.6666666666666666 
"Bob" 
"Dave" 
0.6666666666666666 
"Bob" 
"Alice" 
0.6666666666666666 
"Dave" 
"Alice" 
1.0 
"Dave" 
"Bob" 
0.6666666666666666 
6 rows 
Relationship properties can be used to modify the similarity induced by certain relationships. For example a relationship value of 2 is equal to counting that relationship twice while computing the jaccard similarity.
Weighted jaccard similarity is only defined for values greater or equal to 0. 
The following query will respect relationship properties in the similarity computation:
CALL gds.nodeSimilarity.stream('myGraph', { relationshipWeightProperty: 'strength', similarityCutoff: 0.5 })
YIELD node1, node2, similarity
RETURN gds.util.asNode(node1).name AS Person1, gds.util.asNode(node2).name AS Person2, similarity
ORDER BY Person1
Person1  Person2  similarity 

"Alice" 
"Dave" 
0.8333333333333334 
"Alice" 
"Bob" 
0.8 
"Bob" 
"Alice" 
0.8 
"Bob" 
"Dave" 
0.6666666666666666 
"Dave" 
"Alice" 
0.8333333333333334 
"Dave" 
"Bob" 
0.6666666666666666 
6 rows 
It can be seen that the similarity between Alice and Dave decreased compared to the nonweighted version of this algorithm. This is the case as the strength of the relationship between Alice and Bongos is reduced and both persons now only share 2.5 out of 3 possible instruments. Analogous the similarity between Alice and Bob increased as the missing liked instrument has a lower impact on the similarity score.