extract_node_pagerank
Calculate network 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.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
Calculates the Google PageRank for the specified vertices.
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
Whether the links are directed or not.
The damping factor.
1 - damping is the PageRank value for nodes with no incoming links. It is also the probability of
resetting the random walk to a uniform distribution in each step.Values must be in the following range: