cluster_subnetwork
Identify clusters in the network by filtering the input dataset.
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.
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").
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).
Name of column containing the link targets. Source is implied in the index.
Name of column containing the link weights.
The graphext advanced query syntax used to select rows.
Clustering algorithm to use.
Values must be one of the following:
louvain
The higher this value the bigger the clusters.
Values must be in the following range:
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:
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