Extract node closeness¶
network • graph • centrality
Calculcate network node closeness.
Calculates the closeness centrality for each node in the network. Closeness centrality is a measure of how many steps are required to access every other vertex from a given vertex. In other words, it finds the nodes best placed to influence the entire network most quickly.
Usage¶
The following are the step's expected inputs and outputs and their specific types.
extract_node_closeness(
targets: list[number],
*weights: list[number],
{
"param": value
}
) -> (closeness: 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¶
extract_node_closeness(ds.targets, ds.weights) -> (ds.closeness)
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¶
closeness: column:number
Column containing how many steps is required to access every other vertex from a given vertex.
Parameters¶
mode: string = "all"
Which node connections to count. Whether to
in
: count only a node's incoming linksout
: count only a node's outgoing linksall
/both
count both incoming and outgoing links.
Must be one of:
"all"
,
"out"
,
"in"
,
"both"
normalized: boolean = True
Whether to calculate the normalized closeness.
cutoff: number | null
The maximum path length to consider when calculating the betweenness. If cutoff is zero or negative then there is no such limit.