Extract node pagerank¶

network • graph • centrality

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 are the step's expected inputs and outputs and their specific types.

Step signature

extract_node_pagerank(
    targets: list[number],
    *weights: list[number], {
    "param": value
}) -> (pagerank: number)

where the object {"param": value} is optional in most cases and if present may contain any of the parameters described in the corresponding section below.

Example¶

Example call (in recipe editor)

extract_node_pagerank(ds.targets, ds.weights) -> (ds.page_rank)

Inputs¶

targets: column:list[number]

A column containing link targets. Source is implied in the index.

*weights: column:list[number]

An optional column containing link weights.

Outputs¶

pagerank: column:number

Calculates the Google PageRank for the specified vertices.

Parameters¶

directed: boolean = False

Whether the links are directed or not.

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.

Range: 0 ≤ damping ≤ 1