Skip to main content
At the moment the only supported clustering algorithm is Louvain. Louvain tries to identify the communities in a network by optimizing the modularity of the whole network, that is a measure of the density of edges inside communities to edges outside communities. The result is a column of cluster IDs (integers), where the value -1 is reserved for nodes in very small clusters, which are grouped into a “noise” cluster.

​
Usage

The following example shows how the step can be used in a recipe.

Examples

The following configuration allows for smallish clusters and considers fewish data points as noise:

​
Inputs & Outputs

The following are the inputs expected by the step and the outputs it produces. These are generally columns (ds.first_name), datasets (ds or ds[["first_name", "last_name"]]) or models (referenced by name e.g. "churn-clf").
​
column[list[number]]
required
A column containing link targets. Source is implied in the index.
​
column[list[number]]
​
column
required
A column containing cluster tags.

​
Configuration

The following parameters can be used to configure the behaviour of the step by including them in a json object as the last “input” to the step, i.e. step(..., {"param": "value", ...}) -> (output).

Parameters

​
string
default:"louvain"
Clustering algorithm to use. Only Louvain is currently supported.Values must be one of the following:
  • louvain
​
number
default:"0.5"
The higher this value the bigger the clusters.Values must be in the following range:
​
integer
default:"1"
The larger the value, the more conservative the clustering. Cluster with this number of nodes or less will be considered noise.Values must be in the following range:
​
string
The graphext advanced query syntax used to select rows.