extract_node_closeness
Calculcate network node closeness.
Calculates the closeness centrality for each node in the network. Closeness centrality is a measure of how many steps are required to access every other vertex from a given vertex. In other words, it finds the nodes best placed to influence the entire network most quickly.
Usage
The following example shows how the step can be used in a recipe.
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).
Which node connections to count. Whether to
in: count only a node’s incoming linksout: count only a node’s outgoing linksall/bothcount both incoming and outgoing links.
Values must be one of the following:
alloutinboth
Whether to calculate the normalized closeness.
The maximum path length to consider when calculating the betweenness. If cutoff is zero or negative then there is no such limit.
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