hub algorithm closeness
Runs the closeness algorithm on a model and saves the results for each entity to a model property.
Centrality algorithms measure the importance and significance of individual entities and relationships. When you run centrality algorithms, the value returned by an algorithm indicates importance of an element. The closeness centrality of an entity measures its average farness (inverse distance) to all other entities. Entities with a high closeness score have the shortest distances to all other nodes.
Usage
hub algorithm closeness --m model --d direction --m method --wp weightProperty --lvsignificantLowValues --op outputProperty --w waitForCompleteRequired | Argument | Description |
---|---|---|
Yes | --m model |
Specifies the model. |
No | --d direction | Specifies the direction to apply to the algorithm where direction is one of the following:
|
No | --me method |
Specifies the method in which results are returned:
|
No | --wp weightProperty |
Specifies a relationship property to use to measure how unfavorable a relationship is. By default, a higher value indicates a negative association. The default setting is null. |
No | --lv significantLowValues | If a relationship property is used as weight, this specifies whether a lower value is considered better than a higher value.
|
No | --op outputProperty |
Specifies the output property name to be something other than the algorithm name. The default is Closeness. |
No | --w waitForComplete |
Specifies whether to wait for jobs to complete in a synchronous mode.
|
Example
The following will run the closeness algorithm on the 911 model.
hub algorithm closeness --m 911