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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.

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Usage

The following example shows how the step can be used in a recipe.

Examples

  • Example 1
  • Signature
extract_node_pagerank(ds.targets, ds.weights) -> (ds.page_rank)

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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").
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targets
column[list[number]]
required
A column containing link targets. Source is implied in the index.
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*weights
column[list[number]]
An optional column containing link weights.
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pagerank
column[number]
required
Calculates the Google PageRank for the specified vertices.

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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

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directed
boolean
default:"false"
Whether the links are directed or not.
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damping
number
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:
0 ≤ damping ≤ 1
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