# Degree 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

The Degree Centrality algorithm can be used to find popular nodes within a graph. Degree centrality measures the number of incoming or outgoing (or both) relationships from a node, depending on the orientation of a relationship projection. For more information on relationship orientations, see the relationship projection syntax section.

It can be applied to either weighted or unweighted graphs. In the weighted case the algorithm computes the sum of all positive weights of adjacent relationships of a node, for each node in the graph. Non-positive weights are ignored.

It can be applied to heterogenous graphs, however the algorithm will not calculate degree centrality per relationship type. Instead it will treat the graph as homogenous, as indicated by the algorithm traits.

For more information on this algorithm, see:

## Use-cases

The Degree Centrality algorithm has been shown to be useful in many different applications. For example:

• Degree centrality is an important component of any attempt to determine the most important people in a social network. For example, in BrandWatch’s most influential men and women on Twitter 2017 the top 5 people in each category have over 40m followers each, which is a lot higher than the average degree.

• Weighted degree centrality has been used to help separate fraudsters from legitimate users of an online auction. The weighted centrality for fraudsters is significantly higher because they tend to collude with each other to artificially increase the price of items. Read more in Two Step graph-based semi-supervised Learning for Online Auction Fraud Detection

## Syntax

This section covers the syntax used to execute the Degree 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.

Degree Centrality syntax per mode
Run Degree Centrality in stream mode on a named graph.
``````CALL gds.degree.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.

orientation

String

`NATURAL`

yes

The orientation used to compute node degrees. Supported orientations are `NATURAL`, `REVERSE` and `UNDIRECTED`.

relationshipWeightProperty

String

`null`

yes

Name of the relationship property to use for weighted degree computation. If unspecified, the algorithm runs unweighted.

Table 3. Results
Name Type Description

nodeId

Integer

Node ID.

score

Float

Degree Centrality score.

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

orientation

String

`NATURAL`

yes

The orientation used to compute node degrees. Supported orientations are `NATURAL`, `REVERSE` and `UNDIRECTED`.

relationshipWeightProperty

String

`null`

yes

Name of the relationship property to use for weighted degree computation. If unspecified, the algorithm runs unweighted.

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 Degree Centrality in mutate mode on a named graph.
``````CALL gds.degree.mutate(
graphName: String,
configuration: Map
) YIELD
centralityDistribution: Map,
preProcessingMillis: Integer,
computeMillis: Integer,
postProcessingMillis: 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

mutateProperty

String

`n/a`

no

The node property in the GDS graph to which the degree 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.

orientation

String

`NATURAL`

yes

The orientation used to compute node degrees. Supported orientations are `NATURAL`, `REVERSE` and `UNDIRECTED`.

relationshipWeightProperty

String

`null`

yes

Name of the relationship property to use for weighted degree computation. If unspecified, the algorithm runs unweighted.

Table 9. 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.

mutateMillis

Integer

Milliseconds for adding properties to the projected graph.

nodePropertiesWritten

Integer

Number of properties added to the projected graph.

configuration

Map

Configuration used for running the algorithm.

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

`n/a`

no

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

orientation

String

`NATURAL`

yes

The orientation used to compute node degrees. Supported orientations are `NATURAL`, `REVERSE` and `UNDIRECTED`.

relationshipWeightProperty

String

`null`

yes

Name of the relationship property to use for weighted degree computation. If unspecified, the algorithm runs unweighted.

Table 12. 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.

writeMillis

Integer

Milliseconds for writing result data back.

nodePropertiesWritten

Integer

Number of properties 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 Degree 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 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
(alice:User {name: 'Alice'}),
(bridget:User {name: 'Bridget'}),
(charles:User {name: 'Charles'}),
(doug:User {name: 'Doug'}),
(mark:User {name: 'Mark'}),
(michael:User {name: 'Michael'}),

(alice)-[:FOLLOWS {score: 1}]->(doug),
(alice)-[:FOLLOWS {score: -2}]->(bridget),
(alice)-[:FOLLOWS {score: 5}]->(charles),
(mark)-[:FOLLOWS {score: 1.5}]->(doug),
(mark)-[:FOLLOWS {score: 4.5}]->(michael),
(bridget)-[:FOLLOWS {score: 1.5}]->(doug),
(charles)-[:FOLLOWS {score: 2}]->(doug),
(michael)-[:FOLLOWS {score: 1.5}]->(doug)``````

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 `User` nodes and the `FOLLOWS` relationships.

The following statement will project a graph using a reverse projection and store it in the graph catalog under the name 'myGraph'.
``````CALL gds.graph.project(
'myGraph',
'User',
{
FOLLOWS: {
orientation: 'REVERSE',
properties: ['score']
}
}
)``````

The graph is projected in a `REVERSE` orientation in order to retrieve people with the most followers in the following examples. This will be demonstrated using the Degree Centrality algorithm on this graph.

### Memory Estimation

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 Automatic estimation and execution blocking.

For more details on `estimate` in general, see Memory Estimation.

The following will estimate the memory requirements for running the algorithm:
``````CALL gds.degree.write.estimate('myGraph', { writeProperty: 'degree' })
YIELD nodeCount, relationshipCount, bytesMin, bytesMax, requiredMemory``````
Table 13. Results
nodeCount relationshipCount bytesMin bytesMax requiredMemory

6

8

40

40

"40 Bytes"

### Stream

In the `stream` execution mode, the algorithm returns the degree 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 degree centrality.

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

The following will run the algorithm in `stream` mode:
``````CALL gds.degree.stream('myGraph')
YIELD nodeId, score
RETURN gds.util.asNode(nodeId).name AS name, score AS followers
ORDER BY followers DESC, name DESC``````
Table 14. Results
name followers

"Doug"

5.0

"Michael"

1.0

"Charles"

1.0

"Bridget"

1.0

"Mark"

0.0

"Alice"

0.0

We can see that Doug is the most popular user in our imaginary social network graph, with 5 followers - all other users follow them, but they don’t follow anybody back. In a real social network, celebrities have very high follower counts but tend to follow only very few people. We could therefore consider Doug quite the celebrity!

### 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.degree.stats('myGraph')
YIELD centralityDistribution
RETURN centralityDistribution.min AS minimumScore, centralityDistribution.mean AS meanScore``````
Table 15. Results
minimumScore meanScore

0.0

1.3333358764648438

Comparing this to the results we saw in the stream example, we can find our minimum and mean values from the table.

### 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 degree 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.degree.mutate('myGraph', { mutateProperty: 'degree' })
YIELD centralityDistribution, nodePropertiesWritten
RETURN centralityDistribution.min AS minimumScore, centralityDistribution.mean AS meanScore, nodePropertiesWritten``````
Table 16. Results
minimumScore meanScore nodePropertiesWritten

0.0

1.3333358764648438

6

The returned result is the same as in the `stats` example. Additionally, the graph 'myGraph' now has a node property `degree` which stores the degree centrality score for each node. To find out how to inspect the new schema of the in-memory graph, see Listing graphs in the catalog.

### Write

The `write` execution mode extends the `stats` mode with an important side effect: writing the degree 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.degree.write('myGraph', { writeProperty: 'degree' })
YIELD centralityDistribution, nodePropertiesWritten
RETURN centralityDistribution.min AS minimumScore, centralityDistribution.mean AS meanScore, nodePropertiesWritten``````
Table 17. Results
minimumScore meanScore nodePropertiesWritten

0.0

1.3333358764648438

6

The returned result is the same as in the `stats` example. Additionally, each of the seven nodes now has a new property `degree` in the Neo4j database, containing the degree centrality score for that node.

## Weighted Degree Centrality example

This example will explain the weighted Degree Centrality algorithm. This algorithm is a variant of the Degree Centrality algorithm, that measures the sum of positive weights of incoming and outgoing relationships.

The following will run the algorithm in `stream` mode, showing which users have the highest weighted degree centrality:
``````CALL gds.degree.stream(
'myGraph',
{ relationshipWeightProperty: 'score' }
)
YIELD nodeId, score
RETURN gds.util.asNode(nodeId).name AS name, score AS weightedFollowers
ORDER BY weightedFollowers DESC, name DESC``````
Table 18. Results
name weightedFollowers

"Doug"

7.5

"Charles"

5.0

"Michael"

4.5

"Mark"

0.0

"Bridget"

0.0

"Alice"

0.0

Doug still remains our most popular user, but there isn’t such a big gap to the next person. Charles and Michael both only have one follower, but those relationships have a high relationship weight. Note that Bridget also has a weighted score of 0.0, despite having a connection from Alice. That is because the `score` property value between Bridget and Alice is negative and will be ignored by the algorithm.

## Setting an orientation

By default, node centrality uses the `NATURAL` orientation to compute degrees. For some use-cases it makes sense to analyze a different orientation, for example, if we want to find out how many users follow another user. In order to change the orientation, we can use the `orientation` configuration key. There are three supported values:

• `NATURAL` (default) corresponds to computing the out-degree of each node.

• `REVERSE` corresponds to computing the in-degree of each node.

• `UNDIRECTED` computes and sums both the out-degree and in-degree of each node.

The following will run the algorithm in `stream` mode, showing which users have the highest in-degree centrality using the reverse orientation of the relationships:
``````CALL gds.degree.stream(
'myGraph',
{ orientation: 'REVERSE' }
)
YIELD nodeId, score
RETURN gds.util.asNode(nodeId).name AS name, score AS followees
ORDER BY followees DESC, name DESC``````
Table 19. Results
name followees

"Alice"

3.0

"Mark"

2.0

"Michael"

1.0

"Charles"

1.0

"Bridget"

1.0

"Doug"

0.0

The example shows that when looking at the reverse orientation, `Alice` is more central in the network than `Doug`.