# Closeness Centrality

## 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

Closeness centrality is a way of detecting nodes that are able to spread information very efficiently through a graph.

The closeness centrality of a node measures its average farness (inverse distance) to all other nodes. Nodes with a high closeness score have the shortest distances to all other nodes.

For each node u, the Closeness Centrality algorithm calculates the sum of its distances to all other nodes, based on calculating the shortest paths between all pairs of nodes. The resulting sum is then inverted to determine the closeness centrality score for that node.

The raw closeness centrality of a node u is calculated using the following formula:

`raw closeness centrality(u) = 1 / sum(distance from u to all other nodes)`

It is more common to normalize this score so that it represents the average length of the shortest paths rather than their sum. This adjustment allow comparisons of the closeness centrality of nodes of graphs of different sizes

The formula for normalized closeness centrality of node u is as follows:

`normalized closeness centrality(u) = (number of nodes - 1) / sum(distance from u to all other nodes)`

Wasserman and Faust have proposed an improved formula for dealing with unconnected graphs. Assuming that n is the number of nodes reachable from u (counting also itself), their corrected formula for a given node u is given as follows:

`Wasserman-Faust normalized closeness centrality(u) = (n-1)^2/ number of nodes - 1) * sum(distance from u to all other nodes`

Note that in the case of a directed graph, closeness centrality is defined alternatively. That is, rather than considering distances from u to every other node, we instead sum and average the distance from every other node to u.

### Use-cases - when to use the Closeness Centrality algorithm

• Closeness centrality is used to research organizational networks, where individuals with high closeness centrality are in a favourable position to control and acquire vital information and resources within the organization. One such study is "Mapping Networks of Terrorist Cells" by Valdis E. Krebs.

• Closeness centrality can be interpreted as an estimated time of arrival of information flowing through telecommunications or package delivery networks where information flows through shortest paths to a predefined target. It can also be used in networks where information spreads through all shortest paths simultaneously, such as infection spreading through a social network. Find more details in "Centrality and network flow" by Stephen P. Borgatti.

• Closeness centrality has been used to estimate the importance of words in a document, based on a graph-based keyphrase extraction process. This process is described by Florian Boudin in "A Comparison of Centrality Measures for Graph-Based Keyphrase Extraction".

### Constraints - when not to use the Closeness Centrality algorithm

• Academically, closeness centrality works best on connected graphs. If we use the original formula on an unconnected graph, we can end up with an infinite distance between two nodes in separate connected components. This means that we’ll end up with an infinite closeness centrality score when we sum up all the distances from that node.

In practice, a variation on the original formula is used so that we don’t run into these issues.

## Syntax

This section covers the syntax used to execute the Closeness Centrality 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.

Closeness Centrality syntax per mode
Run Closeness Centrality in stream mode on a named graph.
``````CALL gds.closeness.stream(
graphName: String,
configuration: Map
)
YIELD
nodeId: Integer,
score: Float``````
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.

useWassermanFaust

Boolean

`false`

yes

Use the improved Wasserman-Faust formula for closeness computation.

Table 3. Results
Name Type Description

nodeId

Integer

Node ID.

score

Float

Closeness centrality score.

Run Closeness Centrality in stats mode on a named graph.
``````CALL gds.closeness.stats(
graphName: String,
configuration: Map
)
YIELD
centralityDistribution: Map,
computeMillis: Integer,
postProcessingMillis: Integer,
preProcessingMillis: 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.

useWassermanFaust

Boolean

`false`

yes

Use the improved Wasserman-Faust formula for closeness computation.

Table 6. Results
Name Type Description

centralityDistribution

Map

Map containing min, max, mean as well as p50, p75, p90, p95, p99 and p999 percentile values of centrality values.

preProcessingMillis

Integer

Milliseconds for preprocessing the graph.

computeMillis

Integer

Milliseconds for running the algorithm.

postProcessingMillis

Integer

Milliseconds for computing the statistics.

configuration

Map

Configuration used for running the algorithm.

Run Closeness Centrality in mutate mode on a named graph.
``````CALL gds.closeness.mutate(
graphName: String,
configuration: Map
)
YIELD
nodePropertiesWritten: Integer,
preProcessingMillis: Integer,
computeMillis: Integer,
postProcessingMillis: Integer,
mutateMillis: Integer,
centralityDistribution: Map,
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

mutateProperty

String

`n/a`

no

The node property in the GDS graph to which the centrality is written.

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.

jobId

String

`Generated internally`

yes

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

useWassermanFaust

Boolean

`false`

yes

Use the improved Wasserman-Faust formula for closeness computation.

Table 9. Results
Name Type Description

nodePropertiesWritten

Integer

Number of properties added to the in-memory graph.

preProcessingMillis

Integer

Milliseconds for preprocessing the graph.

computeMillis

Integer

Milliseconds for running the algorithm.

postProcessingMillis

Integer

Milliseconds for computing the statistics.

mutateMillis

Integer

Milliseconds for mutating the GDS graph.

centralityDistribution

Map

Map containing min, max, mean as well as p50, p75, p90, p95, p99 and p999 percentile values of centrality values.

configuration

Map

Configuration used for running the algorithm.

Run Closeness Centrality in write mode on a named graph.
``````CALL gds.closeness.write(
graphName: String,
configuration: Map
)
YIELD
nodePropertiesWritten: Integer,
preProcessingMillis: Integer,
computeMillis: Integer,
postProcessingMillis: Integer,
writeMillis: Integer,
centralityDistribution: Map,
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

`n/a`

no

The node property in the Neo4j database to which the centrality is written.

useWassermanFaust

Boolean

`false`

yes

Use the improved Wasserman-Faust formula for closeness computation.

Table 12. Results
Name Type Description

nodePropertiesWritten

Integer

Number of properties written to Neo4j.

preProcessingMillis

Integer

Milliseconds for preprocessing the graph.

computeMillis

Integer

Milliseconds for running the algorithm.

postProcessingMillis

Integer

Milliseconds for computing the statistics.

writeMillis

Integer

Milliseconds for mutating the GDS graph.

centralityDistribution

Map

Map containing min, max, mean as well as p50, p75, p90, p95, p99 and p999 percentile values of centrality values.

configuration

Map

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 Closeness Centrality 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 sample 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:Node {id:"A"}),
(b:Node {id:"B"}),
(c:Node {id:"C"}),
(d:Node {id:"D"}),
(e:Node {id:"E"}),

With the graph in Neo4j we can now project it into the graph catalog to prepare it for algorithm execution. We do this using a native projection targeting the `Node` nodes and the `LINK` relationships.

The following statement will create a graph using a native projection and store it in the graph catalog under the name 'myGraph'.
``CALL gds.graph.project('myGraph', 'Node', 'LINK')``

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

### Stream

In the `stream` execution mode, the algorithm returns the centrality for each node. This allows us to inspect the results directly or post-process them in Cypher without any side effects. For example, we can order the results to find the nodes with the highest closeness centrality.

For more details on the `stream` mode in general, see Stream.

The following will run the algorithm in `stream` mode:
``````CALL gds.closeness.stream('myGraph')
YIELD nodeId, score
RETURN gds.util.asNode(nodeId).id AS id, score
ORDER BY score DESC``````
Table 13. Results
id score

"C"

0.6666666666666666

"B"

0.5714285714285714

"D"

0.5714285714285714

"A"

0.4

"E"

0.4

C is the best connected node in this graph, although B and D aren’t far behind. A and E don’t have close ties to many other nodes, so their scores are lower. Any node that has a direct connection to all other nodes would score 1.

### 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.

The following will run the algorithm in `stats` mode:
``````CALL gds.closeness.stats('myGraph')
YIELD centralityDistribution
RETURN centralityDistribution.min AS minimumScore, centralityDistribution.mean AS meanScore``````
Table 14. Results
minimumScore meanScore

0.399999618530273

0.521904373168945

### Mutate

The `mutate` execution mode extends the `stats` mode with an important side effect: updating the named graph with a new node property containing the centrality for that node. 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.

The following will run the algorithm in `mutate` mode:
``````CALL gds.closeness.mutate('myGraph', { mutateProperty: 'centrality' })
YIELD centralityDistribution, nodePropertiesWritten
RETURN centralityDistribution.min AS minimumScore, centralityDistribution.mean AS meanScore, nodePropertiesWritten``````
Table 15. Results
minimumScore meanScore nodePropertiesWritten

0.399999618530273

0.521904373168945

5

### Write

The `write` execution mode extends the `stats` mode with an important side effect: writing the centrality for each node as a property to the Neo4j database. 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. The `write` mode enables directly persisting the results to the database.

For more details on the `write` mode in general, see Write.

The following will run the algorithm in `write` mode:
``````CALL gds.closeness.write('myGraph', { writeProperty: 'centrality' })
YIELD centralityDistribution, nodePropertiesWritten
RETURN centralityDistribution.min AS minimumScore, centralityDistribution.mean AS meanScore, nodePropertiesWritten``````
Table 16. Results
minimumScore meanScore nodePropertiesWritten

0.399999618530273

0.521904373168945

5