Centrality Algorithms
Centrality algorithms measure the importance and significance, also known as the centrality, of individual entities and relationships in a model. Use these algorithms to distinguish leaders and followers, influencers and outliers, and similar characteristics of your data.
The Command Line Interface in the Administration Utility and the Relationship Analysis Client provide four kinds of centrality measures to apply to your model:
- Betweenness
- Used to identify entities that control the information flow between different parts of the network.
- Closeness
- Used to identify entities that may have best access to other parts of the network and visibility of activities within the rest of the network.
- Degree
- Used to identify entities that have the most direct links to others.
- Influence
- Used to identify entities that have strong influence in the network due to their direct links to other highly active or well-connected entities.
There are three types of directions an algorithm can be run:
- Incoming
- The results will be based on relationships coming into the entity.
- Outgoing
- The results will be based on relationships going out of the entity.
- Both
- The results will be based on incoming and outgoing relationships.
There are additional measures that can take place with centrality algorithms. For instance, you can select, roughly, how precise the results should be. A lower precision will return more accurate results, but the algorithm will run more slowly. With the Closeness algorithm, you can select how the results should be returned. You can also designate that the relationship property should be used as weight or that low values should be considered more significant, such as if you were using data that ranks entities, and #1 was the best possible ranking.
For details about using the Centrality algorithms, refer to the following table.
| Method | See |
|---|---|
| Relationship Analysis Client |