HITS

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

Supported algorithm traits:

Directed

Undirected

Heterogeneous nodes

Heterogeneous relationships

Weighted relationships

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.

1. 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.

2. 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.

3. 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 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:

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.

3.1. 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