Overlap Similarity

This section describes the Overlap Similarity algorithm in the Neo4j Graph Data Science library.

Overlap similarity measures overlap between two sets. It is defined as the size of the intersection of two sets, divided by the size of the smaller of the two sets.

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

1. History and explanation

Overlap similarity is computed using the following formula:

The library contains both procedures and functions to calculate similarity between sets of data. The function is best used when calculating the similarity between small numbers of sets. The procedures parallelize the computation, and are therefore more appropriate for computing similarities on bigger datasets.

2. Use-cases - when to use the Overlap Similarity algorithm

We can use the Overlap Similarity algorithm to work out which things are subsets of others. We might then use these computed subsets to learn a taxonomy from tagged data, as described by Jesús Barrasa.

3. Overlap Similarity algorithm function sample

The following will return the Overlap similarity of two lists of numbers:

RETURN gds.alpha.similarity.overlap([1,2,3], [1,2,4,5]) AS similarity

Table 1. Results
`similarity`
0.6666666666666666

These two lists of numbers have an overlap similarity of 0.66. We can see how this result is derived by breaking down the formula:

O(A,B) = (∣A ∩ B∣) / (min(∣A|,|B|))
O(A,B) = 2 / min(3,4)
       = 2 / 3
       = 0.66

4. Overlap Similarity algorithm procedures sample

The following will create a sample graph:

CREATE
(fahrenheit451:Book {title:'Fahrenheit 451'}),
(dune:Book {title:'Dune'}),
(hungerGames:Book {title:'The Hunger Games'}),
(nineteen84:Book {title:'1984'}),
(gatsby:Book {title:'The Great Gatsby'}),

(scienceFiction:Genre {name: "Science Fiction"}),
(fantasy:Genre {name: "Fantasy"}),
(dystopia:Genre {name: "Dystopia"}),
(classics:Genre {name: "Classics"}),

(fahrenheit451)-[:HAS_GENRE]->(dystopia),
(fahrenheit451)-[:HAS_GENRE]->(scienceFiction),
(fahrenheit451)-[:HAS_GENRE]->(fantasy),
(fahrenheit451)-[:HAS_GENRE]->(classics),

(hungerGames)-[:HAS_GENRE]->(scienceFiction),
(hungerGames)-[:HAS_GENRE]->(fantasy),

(nineteen84)-[:HAS_GENRE]->(scienceFiction),
(nineteen84)-[:HAS_GENRE]->(dystopia),
(nineteen84)-[:HAS_GENRE]->(classics),

(dune)-[:HAS_GENRE]->(scienceFiction),
(dune)-[:HAS_GENRE]->(fantasy),
(dune)-[:HAS_GENRE]->(classics),

 (gatsby)-[:HAS_GENRE]->(classics)

4.1. Stream

The following will return a stream of node pairs, along with their intersection and overlap similarities:

MATCH (book:Book)-[:HAS_GENRE]->(genre)
WITH {item:id(genre), categories: collect(id(book))} AS userData
WITH collect(userData) AS data
CALL gds.alpha.similarity.overlap.stream({data: data})
YIELD item1, item2, count1, count2, intersection, similarity
RETURN gds.util.asNode(item1).name AS from, gds.util.asNode(item2).name AS to,
       count1, count2, intersection, similarity
ORDER BY similarity DESC

Table 2. Results
`from`	`to`	`count1`	`count2`	`intersection`	`similarity`
Fantasy	Science Fiction	3	4	3	1.0
Dystopia	Science Fiction	2	4	2	1.0
Dystopia	Classics	2	4	2	1.0
Science Fiction	Classics	4	4	3	0.75
Fantasy	Classics	3	4	2	0.66
Dystopia	Fantasy	2	3	1	0.5

Fantasy and Dystopia are both clear subgenres of Science Fiction - 100% of the books that list those as genres also list Science Fiction as a genre. Dystopia is also a subgenre of Classics. The others are less obvious; Dystopia probably isn’t a subgenre of Fantasy, but the other two pairs could be subgenres.

The following will return a stream of node pairs that have a similarity of at least 0.75, along with their intersection and overlap similarities:

MATCH (book:Book)-[:HAS_GENRE]->(genre)
WITH {item:id(genre), categories: collect(id(book))} AS userData
WITH collect(userData) AS data
CALL gds.alpha.similarity.overlap.stream({
  data: data,
  similarityCutoff: 0.75
})
YIELD item1, item2, count1, count2, intersection, similarity
RETURN gds.util.asNode(item1).name AS from, gds.util.asNode(item2).name AS to,
       count1, count2, intersection, similarity
ORDER BY similarity DESC

Table 3. Results
`from`	`to`	`count1`	`count2`	`intersection`	`similarity`
Fantasy	Science Fiction	3	4	3	1.0
Dystopia	Classics	2	4	2	1.0
Dystopia	Science Fiction	2	4	2	1.0
Science Fiction	Classics	4	4	3	0.75

We can see that those genres with lower similarity have been filtered out. If we’re implementing a k-Nearest Neighbors type query we might instead want to find the most similar k super genres for a given genre. We can do that by passing in the topK parameter.

The following will return a stream of genres, along with the two most similar super genres to them (i.e. k=2):

MATCH (book:Book)-[:HAS_GENRE]->(genre)
WITH {item:id(genre), categories: collect(id(book))} AS userData
WITH collect(userData) AS data
CALL gds.alpha.similarity.overlap.stream({
 data: data,
 topK: 2
})
YIELD item1, item2, count1, count2, intersection, similarity
RETURN gds.util.asNode(item1).name AS from, gds.util.asNode(item2).name AS to,
       count1, count2, intersection, similarity
ORDER BY from

Table 4. Results
`from`	`to`	`count1`	`count2`	`intersection`	`similarity`
Dystopia	Classics	2	4	2	1.0
Dystopia	Science Fiction	2	4	2	1.0
Fantasy	Science Fiction	3	4	3	1.0
Fantasy	Classics	3	4	2	0.6666666666666666
Science Fiction	Classics	4	4	3	0.75

4.2. Write

The following will find the most similar genre for each genre, and store a relationship between those genres:

MATCH (book:Book)-[:HAS_GENRE]->(genre)
WITH {item:id(genre), categories: collect(id(book))} AS userData
WITH collect(userData) AS data
CALL gds.alpha.similarity.overlap.write({
 data: data,
 topK: 2,
 similarityCutoff: 0.5
})
YIELD nodes, similarityPairs, writeRelationshipType, writeProperty, min, max, mean, stdDev, p25, p50, p75, p90, p95, p99, p999, p100
RETURN nodes, similarityPairs, writeRelationshipType, writeProperty, min, max, mean, p95

Table 5. Results
`nodes`	`similarityPairs`	`writeRelationshipType`	`writeProperty`	`min`	`max`	`mean`	`p95`
4	5	NARROWER_THAN	score	0.6666641235351562	1.0000038146972656	0.8833351135253906	1.0000038146972656

We then could write a query to find out the genre hierarchy for a specific genre.

The following will find the genre hierarchy for the Fantasy genre

MATCH path = (fantasy:Genre {name: "Fantasy"})-[:NARROWER_THAN*]->(genre)
RETURN [node in nodes(path) | node.name] AS hierarchy
ORDER BY length(path)

Table 6. Results
`hierarchy`
["Fantasy", "Science Fiction"]
["Fantasy", "Classics"]
["Fantasy", "Science Fiction", "Classics"]

4.3. Stats

The following will run the algorithm and returns the result in form of statistical and measurement values

MATCH (book:Book)-[:HAS_GENRE]->(genre)
WITH {item:id(genre), categories: collect(id(book))} AS userData
WITH collect(userData) AS data
CALL gds.alpha.similarity.overlap.stats({
 data: data,
 topK: 2,
 similarityCutoff: 0.5
})
YIELD nodes, similarityPairs, writeRelationshipType, writeProperty, min, max, mean, p95
RETURN nodes, similarityPairs, writeRelationshipType, writeProperty, min, max, mean, p95

5. Specifying source and target ids

Sometimes, we don’t want to compute all pairs similarity, but would rather specify subsets of items to compare to each other. We do this using the sourceIds and targetIds keys in the config.

We could use this technique to compute the similarity of a subset of items to all other items.

The following will return the super genres for the Fantasy and Classics genres:

MATCH (book:Book)-[:HAS_GENRE]->(genre)
WITH {item:id(genre), name: genre.name, categories: collect(id(book))} AS userData
WITH collect(userData) AS data
WITH data,
     [value in data WHERE value.name IN ["Fantasy", "Classics"] | value.item ] AS sourceIds
CALL gds.alpha.similarity.overlap.stream({
 data: data,
 sourceIds: sourceIds
})
YIELD item1, item2, count1, count2, intersection, similarity
RETURN gds.util.asNode(item1).name AS from, gds.util.asNode(item2).name AS to, similarity
ORDER BY similarity DESC

Table 7. Results
`from`	`to`	`similarity`
Fantasy	Science Fiction	1.0
Classics	Science Fiction	0.75
Fantasy	Classics	0.6666666666666666

6. Syntax

The following will run the algorithm and write back results:

CALL gds.alpha.similarity.overlap.write(configuration: Map)
YIELD nodes, similarityPairs, writeRelationshipType, writeProperty, min, max, mean, stdDev, p25, p50, p75, p90, p95, p99, p999, p100

Table 8. Parameters
Name	Type	Default	Optional	Description
configuration	Map	n/a	no	Algorithm-specific configuration.

Table 9. Configuration
Name	Type	Default	Optional	Description
data	List or String	null	no	A list of maps of the following structure: `{item: nodeId, weights: [double, double, double]}` or a Cypher query.
top	Integer	0	yes	The number of similar pairs to return. If `0`, it will return as many as it finds.
topK	Integer	3	yes	The number of similar values to return per node. If `0`, it will return as many as it finds.
similarityCutoff	Integer	-1	yes	The threshold for similarity. Values below this will not be returned.
degreeCutoff	Integer	0	yes	The threshold for the number of items in the `targets` list. If the list contains less than this amount, that node will be excluded from the calculation.
skipValue	Float	gds.util.NaN()	yes	Value to skip when executing similarity computation. A value of `null` means that skipping is disabled.
concurrency	Integer	4	yes	The number of concurrent threads used for running the algorithm. Also provides the default value for 'writeConcurrency'.
writeConcurrency	Integer	value of 'concurrency'	yes	The number of concurrent threads used for writing the result.
graph	String	dense	yes	The graph name ('dense' or 'cypher').
writeBatchSize	Integer	10000	yes	The batch size to use when storing results.
writeRelationshipType	String	SIMILAR	yes	The relationship type to use when storing results.
writeProperty	String	score	yes	The property to use when storing results.
sourceIds	String[]	null	yes	The ids of items from which we need to compute similarities. Defaults to all the items provided in the `data` parameter.
targetIds	String[]	null	yes	The ids of items to which we need to compute similarities. Defaults to all the items provided in the `data` parameter.

Table 10. Results
Name	Type	Description
nodes	Integer	The number of nodes passed in.
similarityPairs	Integer	The number of pairs of similar nodes computed.
writeRelationshipType	String	The relationship type used when storing results.
writeProperty	String	The property used when storing results.
min	Float	The minimum similarity score computed.
max	Float	The maximum similarity score computed.
mean	Float	The mean of similarities scores computed.
stdDev	Float	The standard deviation of similarities scores computed.
p25	Float	The 25 percentile of similarities scores computed.
p50	Float	The 50 percentile of similarities scores computed.
p75	Float	The 75 percentile of similarities scores computed.
p90	Float	The 90 percentile of similarities scores computed.
p95	Float	The 95 percentile of similarities scores computed.
p99	Float	The 99 percentile of similarities scores computed.
p999	Float	The 99.9 percentile of similarities scores computed.
p100	Float	The 100 percentile of similarities scores computed.

The following will run the algorithm and stream results:

CALL gds.alpha.similarity.overlap.stream(configuration: Map)
YIELD item1, item2, count1, count2, similarity

Table 11. Parameters
Name	Type	Default	Optional	Description
configuration	Map	n/a	no	Algorithm-specific configuration.

Table 12. Configuration
Name	Type	Default	Optional	Description
data	List or String	null	no	A list of maps of the following structure: `{item: nodeId, weights: [double, double, double]}` or a Cypher query.
top	Integer	0	yes	The number of similar pairs to return. If `0`, it will return as many as it finds.
topK	Integer	3	yes	The number of similar values to return per node. If `0`, it will return as many as it finds.
similarityCutoff	Integer	-1	yes	The threshold for similarity. Values below this will not be returned.
degreeCutoff	Integer	0	yes	The threshold for the number of items in the `targets` list. If the list contains less than this amount, that node will be excluded from the calculation.
skipValue	Float	gds.util.NaN()	yes	Value to skip when executing similarity computation. A value of `null` means that skipping is disabled.
concurrency	Integer	4	yes	The number of concurrent threads used for running the algorithm.
graph	String	dense	yes	The graph name ('dense' or 'cypher').
sourceIds	Integer[]	null	yes	The ids of items from which we need to compute similarities. Defaults to all the items provided in the `data` parameter.
targetIds	Integer[]	null	yes	The ids of items to which we need to compute similarities. Defaults to all the items provided in the `data` parameter.

Table 13. Results
Name	Type	Description
item1	Integer	The ID of one node in the similarity pair.
item2	Integer	The ID of other node in the similarity pair.
count1	Integer	The size of the `targets` list of one node.
count2	Integer	The size of the `targets` list of other node.
intersection	Integer	The number of intersecting values in the two nodes `targets` lists.
similarity	Integer	The overlap similarity of the two nodes.

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