extract_node_pagerank

Calculates the PageRank centrality for each node in the network. PageRank is a measure of the importance of a node in a network. It is based on the idea that a node is important if it is linked to by other important nodes. The algorithm is iterative and the importance of a node is calculated as the sum of the importance of the nodes that link to it. The importance of a node is then distributed to the nodes it links to. The algorithm is run until convergence. A damping factor is used to avoid the problem of dead ends.

For more information about the algorithm and its parameters see the wikipedia entry or the original paper here.

​

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).