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.Examples
Examples
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").
Inputs
Inputs
Outputs
Outputs
Column containing how many steps is required to access every other vertex from a given vertex.
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
Parameters
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.
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.