Conductance metric

This algorithm is in the alpha tier. For more information on algorithm tiers, see Graph algorithms.

Supported algorithm traits:

1. Introduction

Conductance 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. The conductance is the ratio between relationships that point outside C and the total number of relationships of C. The lower the conductance, the more "well-knit" a community is.

It was shown by Yang and Leskovec in the paper "Defining and Evaluating Network Communities based on Ground-truth" that conductance is a very good metric for evaluating actual communities of real world graphs.

The algorithm runs in time linear to the number of relationships in the graph.

2. Syntax

This section covers the syntax used to execute the Conductance 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.

Example 1. Conductance syntax per mode
Run Conductance in stream mode on a named graph.
  graphName: String,
  configuration: Map
  community: Integer,
  conductance: Float
Table 1. Parameters
Name Type Default Optional Description





The name of a graph stored in the catalog.





Configuration for algorithm-specifics and/or graph filtering.

Table 2. Configuration
Name Type Default Optional Description


List of String



Filter the named graph using the given node labels.


List of String



Filter the named graph using the given relationship types.





The number of concurrent threads used for running the algorithm.



Generated internally


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





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 conductance computed.

Table 3. Results
Name Type Description



Community ID.



Conductance of the community.

Only non-negative community IDs are valid for identifying communities. Nodes with a negative community ID will only take part in the computation to the extent that they are connected to nodes in valid communities, and thus contribute to those valid communities' outward relationship counts.

3. Examples

In this section we will show examples of running the Conductance 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:
  (nAlice:User {name: 'Alice', seed: 42}),
  (nBridget:User {name: 'Bridget', seed: 42}),
  (nCharles:User {name: 'Charles', seed: 42}),
  (nDoug:User {name: 'Doug'}),
  (nMark:User {name: 'Mark'}),
  (nMichael:User {name: 'Michael'}),

  (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 two clusters of Users, that are closely connected. Between those clusters there is one single edge. 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 as this works best with the Louvain algorithm which we will use to create the communities that we evaluate using Conductance.

In the examples below we will use named graphs and native projections as the norm. However, Cypher projections can also be used.

The following statement will project the graph and store it in the graph catalog.
CALL gds.graph.project(
        LINK: {
            orientation: 'UNDIRECTED'
        nodeProperties: 'seed',
        relationshipProperties: 'weight'

We now run the Louvain algorithm to create a division of the nodes into communities that we can then evalutate.

The following will run the Louvain algorithm and store the results in myGraph:
CALL gds.louvain.mutate('myGraph', { mutateProperty: 'community', relationshipWeightProperty: 'weight' })
YIELD communityCount
Table 4. Results


Now our in-memory graph myGraph is populated with node properties under the key community that we can set as input for our evaluation using Conductance. The nodes are now assigned to communities in the following way:

Table 5. Community assignments
name community













Please see the stream node properties procedure for how to obtain such an assignment table.

For more information about Louvain, see its algorithm page.

3.1. Stream

Since we now have a community detection, we can evaluate how good it is under the conductance metric. Note that we in this case we use the feature of relationships being weighted by a relationship property.

The Conductance stream procedure returns the conductance 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.

The following will run the Conductance algorithm in stream mode:
CALL'myGraph', { communityProperty: 'community', relationshipWeightProperty: 'weight' })
YIELD community, conductance
Table 6. Results
community conductance







We can see that the community of the weighted graph with the lowest conductance 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.