extract_node_degree
Calculate network node degrees.
Calculates the degree centrality of each node in the network, i.e. the number of each node’s incoming and/or outgoing connections.
Usage
The following example shows how the step can be used in a recipe.
E.g. in a network of twitter accounts, where a directed link between nodes A and B indicates the number of times A has retweeted B, the following calculates the total number of retweets each account has received: Since in the example network links A→B and B→A can be different, we indicate that we want to interpret the network as directed. And since each link’s weight is the number of retweets, we pass the retweets weight as the weights column. Keep in mind that both columns contain lists of numbers for each row.
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 the links are directed or not.
Whether loops will be counted. Loops are links of nodes to themselves.
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