Using Centrality

Centrality is a way of measuring the importance and significance of individual entities and relationships. When you run centrality algorithms, the higher the value, the more important the element.
  1. From the Centrality ribbon on the Data tab, select the kind of centrality measure you want to apply to your model.
    • Betweenness—Reflects the number of shortest paths between one entity and other entities.
    • Closeness—Reflects the length of geodesic distances between one entity and other entities.
    • Degree—Reflects the number of relationships on a entity.
    • Influence—Reflects the importance of an entity, based on its connections to high-scoring entities.
  2. Select the direction in which you want to apply the algorithm:
    • 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 relationships both coming into and going out of the entity.
  3. If you are using a Closeness algorithm, click the appropriate button for the way in which you want results to be returned:
    • Standard—Results are based on the number of attachments, or relationships, an entity has as well as the reverse of the sum of shortest paths to each entity.
    • Dangalchev—Results are based not only on the number of entities linked to another entity but also the number of relationships in each of the linked entities.
    • Opsahl—Results are based on the sum of reversed shortest paths to each entity.
  4. If you are using an Influence algorithm, slide the Precision scale to determine how precise the results should be. A lower precision will return more accurate results, but the algorithm will run more slowly.
  5. Click the Use relationship property as weight if you want to measure how unfavorable a relationship is, and select the relationship property you want to use from the Property drop-down. In this case, a higher value indicates a negative association.
  6. Click the Low values are more significant box if you are using a relationship property as weight and that property is one where a lower value is considered better than a higher value. For example, if the property is some sort of ranking system, typically 1, or 1st, is the best value. Another example is if the property is distance, and you are trying to determine the shortest route: 5 miles is considered better than 10 miles.
  7. Click the Override default output property name if you want the output property name to be something other than the algorithm you selected. Then enter the new name in the Property field.
  8. Click OK.