HITS

This feature is in the alpha tier. For more information on feature tiers, see API Tiers.

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 Hyperlink-Induced Topic Search (HITS) is a link analysis algorithm that rates nodes based on two scores, a hub score and an authority score. The authority score estimates the importance of the node within the network. The hub score estimates the value of its relationships to other nodes. The GDS implementation is based on the Authoritative Sources in a Hyperlinked Environment publication by Jon M. Kleinberg.

The HITS algorithm requires the inverse index for each relationship type.

Syntax

This section covers the syntax used to execute the HITS 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.

HITS syntax per mode
Run HITS in stream mode on a named graph.
CALL gds.hits.stream(
  graphName: String,
  configuration: Map
)
YIELD
  nodeId: Integer,
  values: Map
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. 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.

hitsIterations

Integer

20

yes

The number of hits iterations to run. The number of pregel iterations will be equal to hitsIterations * 4

authProperty

String

"auth"

yes

The name that is used for the auth property when using STREAM, MUTATE or WRITE modes.

hubProperty

String

"hub"

yes

The name that is used for the hub property when using STREAM, MUTATE or WRITE modes.

partitioning

String

"AUTO"

yes

The partitioning scheme used to divide the work between threads. Available options are AUTO, RANGE, DEGREE.

Table 3. Results
Name Type Description

nodeId

Integer

Node ID.

values

Map

A map containing the auth and hub keys.

Run HITS in stats mode on a named graph.
CALL gds.hits.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. 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.

hitsIterations

Integer

20

yes

The number of hits iterations to run. The number of pregel iterations will be equal to hitsIterations * 4

authProperty

String

"auth"

yes

The name that is used for the auth property when using STREAM, MUTATE or WRITE modes.

hubProperty

String

"hub"

yes

The name that is used for the hub property when using STREAM, MUTATE or WRITE modes.

partitioning

String

"AUTO"

yes

The partitioning scheme used to divide the work between threads. Available options are AUTO, RANGE, DEGREE.

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 HITS in mutate mode on a named graph.
CALL gds.hits.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.

hitsIterations

Integer

20

yes

The number of hits iterations to run. The number of pregel iterations will be equal to hitsIterations * 4

authProperty

String

"auth"

yes

The name that is used for the auth property when using STREAM, MUTATE or WRITE modes.

hubProperty

String

"hub"

yes

The name that is used for the hub property when using STREAM, MUTATE or WRITE modes.

partitioning

String

"AUTO"

yes

The partitioning scheme used to divide the work between threads. Available options are AUTO, RANGE, DEGREE.

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 HITS in write mode on a named graph.
CALL gds.hits.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. 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.

writeProperty

String

""

yes

The prefix used for all public properties in the PregelSchema.

hitsIterations

Integer

20

yes

The number of hits iterations to run. The number of pregel iterations will be equal to hitsIterations * 4

authProperty

String

"auth"

yes

The name that is used for the auth property when using STREAM, MUTATE or WRITE modes.

hubProperty

String

"hub"

yes

The name that is used for the hub property when using STREAM, MUTATE or WRITE modes.

partitioning

String

"AUTO"

yes

The partitioning scheme used to divide the work between threads. Available options are AUTO, RANGE, DEGREE.

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.

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 HITS 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:

Visualization of the example graph
The following Cypher statement will create the example graph in the Neo4j database:
CREATE
  (a:Website {name: 'A'}),
  (b:Website {name: 'B'}),
  (c:Website {name: 'C'}),
  (d:Website {name: 'D'}),
  (e:Website {name: 'E'}),
  (f:Website {name: 'F'}),
  (g:Website {name: 'G'}),
  (h:Website {name: 'H'}),
  (i:Website {name: 'I'}),

  (a)-[:LINK]->(b),
  (a)-[:LINK]->(c),
  (a)-[:LINK]->(d),
  (b)-[:LINK]->(c),
  (b)-[:LINK]->(d),
  (c)-[:LINK]->(d),

  (e)-[:LINK]->(b),
  (e)-[:LINK]->(d),
  (e)-[:LINK]->(f),
  (e)-[:LINK]->(h),

  (f)-[:LINK]->(g),
  (f)-[:LINK]->(i),
  (f)-[:LINK]->(h),
  (g)-[:LINK]->(h),
  (g)-[:LINK]->(i),
  (h)-[:LINK]->(i);

In the example, we will use the HITS algorithm to calculate the authority and hub scores.

The following statement will project the graph and store it in the graph catalog.
CALL gds.graph.project(
  'myGraph',
  'Website',
  {LINK: {indexInverse: true}}
);

In the following examples we will demonstrate using the HITS algorithm on this graph.

Stream

In the stream execution mode, the algorithm returns the authority and hub scores 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.hits.stream('myGraph', {hitsIterations: 20})
YIELD nodeId, values
RETURN gds.util.asNode(nodeId).name AS Name, values.auth AS auth, values.hub as hub
ORDER BY Name ASC
Table 13. Results
Name auth hub

"A"

0.0

0.5147630377521207

"B"

0.42644630743935796

0.3573686670593437

"C"

0.3218729455718005

0.23857061715828276

"D"

0.6463862608483191

0.0

"E"

0.0

0.640681017095129

"F"

0.23646490227616518

0.2763222153580397

"G"

0.10200264424057169

0.23867470447760597

"H"

0.426571816146601

0.0812340105698113

"I"

0.22009646020698218

0.0