Modularity metric
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.
1. Introduction
Modularity is a metric that allows you to evaluate the quality of a community detection.
Relationships of nodes in a community C
connect to nodes either within C
or outside C
.
Graphs with high modularity have dense connections between the nodes within communities but sparse connections between nodes in different communities.
2. Syntax
This section covers the syntax used to execute the Modularity Metric 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.
CALL gds.alpha.modularity.stream(
graphName: String,
configuration: Map
) YIELD
communityId: Integer,
modularity: Float
Name | Type | Default | Optional | Description |
---|---|---|---|---|
graphName |
String |
|
no |
The name of a graph stored in the catalog. |
configuration |
Map |
|
yes |
Configuration for algorithm-specifics and/or graph filtering. |
Name | Type | Default | Optional | Description |
---|---|---|---|---|
List of String |
|
yes |
Filter the named graph using the given node labels. |
|
List of String |
|
yes |
Filter the named graph using the given relationship types. |
|
Integer |
|
yes |
The number of concurrent threads used for running the algorithm. |
|
String |
|
yes |
An ID that can be provided to more easily track the algorithm’s progress. |
|
Boolean |
|
yes |
If disabled the progress percentage will not be logged. |
|
String |
|
yes |
Name of the relationship property to use as weights. If unspecified, the algorithm runs unweighted. |
|
communityProperty |
String |
|
no |
The node property that holds the community ID as an integer for each node. Note that only non-negative community IDs are considered valid and will have their modularity score computed. |
Name | Type | Description |
---|---|---|
communityId |
Integer |
Community ID. |
modularity |
Float |
Modularity of the community. |
CALL gds.alpha.modularity.stats(
graphName: String,
configuration: Map
) YIELD
nodeCount: Integer,
relationshipCount: Integer,
communityCount: Integer,
modularity: Float,
postProcessingMillis: Integer,
preProcessingMillis: Integer,
computeMillis: Integer,
configuration: Map
Name | Type | Default | Optional | Description |
---|---|---|---|---|
graphName |
String |
|
no |
The name of a graph stored in the catalog. |
configuration |
Map |
|
yes |
Configuration for algorithm-specifics and/or graph filtering. |
Name | Type | Default | Optional | Description |
---|---|---|---|---|
List of String |
|
yes |
Filter the named graph using the given node labels. |
|
List of String |
|
yes |
Filter the named graph using the given relationship types. |
|
Integer |
|
yes |
The number of concurrent threads used for running the algorithm. |
|
String |
|
yes |
An ID that can be provided to more easily track the algorithm’s progress. |
|
Boolean |
|
yes |
If disabled the progress percentage will not be logged. |
|
String |
|
yes |
Name of the relationship property to use as weights. If unspecified, the algorithm runs unweighted. |
|
communityProperty |
String |
|
no |
The node property that holds the community ID as an integer for each node. Note that only non-negative community IDs are considered valid and will have their modularity score computed. |
Name | Type | Description |
---|---|---|
nodeCount |
Integer |
The number of nodes in the graph. |
relationshipCount |
Integer |
The number of relationships in the graph. |
communityCount |
Integer |
The number of communities. |
modularity |
Float |
The total modularity score. |
preProcessingMillis |
Integer |
Milliseconds for preprocessing the data. |
computeMillis |
Integer |
Milliseconds for running the algorithm. |
postProcessingMillis |
Integer |
Milliseconds for computing percentiles and community count. |
configuration |
Map |
The configuration used for running the algorithm. |
3. Examples
In this section we will show examples of running the Modularity 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:
CREATE
(nAlice:User {name: 'Alice', community: 3}),
(nBridget:User {name: 'Bridget', community: 2}),
(nCharles:User {name: 'Charles', community: 2}),
(nDoug:User {name: 'Doug', community: 3}),
(nMark:User {name: 'Mark', community: 5}),
(nMichael:User {name: 'Michael', community: 5}),
(nAlice)-[:LINK {weight: 1}]->(nBridget),
(nAlice)-[:LINK {weight: 1}]->(nCharles),
(nCharles)-[:LINK {weight: 1}]->(nBridget),
(nAlice)-[:LINK {weight: 5}]->(nDoug),
(nMark)-[:LINK {weight: 1}]->(nDoug),
(nMark)-[:LINK {weight: 1}]->(nMichael),
(nMichael)-[:LINK {weight: 1}]->(nMark);
This graph has three pre-computed communities of Users, that are closely connected.
For more details on the available community detection algorithms, please refer to Community algorithms section of the documentation.
The communities are indicated by the community
node property on each node.
The relationships that connect the nodes in each component have a property weight
which determines the strength of the relationship.
We can now project the graph and store it in the graph catalog.
We load the LINK
relationships with orientation set to UNDIRECTED
.
In the examples below we will use named graphs and native projections as the norm. However, Cypher projections can also be used. |
CALL gds.graph.project(
'myGraph',
'User',
{
LINK: {
orientation: 'UNDIRECTED'
}
},
{
nodeProperties: 'community',
relationshipProperties: 'weight'
}
)
3.1. Stream
Since we have community information on each node, we can evaluate how good it is under the modularity metric. Note that we in this case we use the feature of relationships being weighted by a relationship property.
The Modularity stream procedure returns the modularity for each community. 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.
stream
mode:CALL gds.alpha.modularity.stream('myGraph', { communityProperty: 'community', relationshipWeightProperty: 'weight' })
YIELD communityId, modularity
RETURN communityId, modularity
ORDER BY communityId ASC
communityId | modularity |
---|---|
2 |
0.057851239669421 |
3 |
0.105371900826446 |
5 |
0.130165289256198 |
We can see that the community of the weighted graph with the highest modularity is community 5. This means that 5 is the community that is most "well-knit" in the sense that most of its relationship weights are internal to the community.
3.2. Stats
For more details on the stream mode in general, see Stats.
stats
mode:CALL gds.alpha.modularity.stats('myGraph', { communityProperty: 'community', relationshipWeightProperty: 'weight' })
YIELD nodeCount, relationshipCount, communityCount, modularity
nodeCount | relationshipCount | communityCount | modularity |
---|---|---|---|
6 |
14 |
3 |
0.293388429752066 |
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