Centrality metrics help identify the most critical or influential nodes in a network. Applications include finding the most highly connected individual on a social media platform, which piece of critical infrastructure provides the largest impact to a network if degraded, and helping to identify diseaserelated superspreading events and contact tracing.
Once you have a link chart with at least one node set and one link set connecting your nodes, follow these steps to perform centrality analysis:

Click Link Analysis on the Link Chart Diagram tab to open the analysis tools window.
To minimize the window, click the arrow at the upper left.
 Under Analysis Method, choose Centrality.
 Next to Centrality, choose one of the following options:
 Betweenness Centrality—How often a node lies on the shortest path between each pair of nodes in the network. Removing or adding a central node may result in the shortest paths to change, necessitating recalculation of the betweenness centrality. Betweenness centrality allows for the identification of nodes that act as connecting nodes between nodes in a network.
Betweenness centrality can help answer the following questions:
 Which individual or individuals connect various cells of a given criminal network?
 Which airport's closure would have the greatest overall impact to an air transportation network in each country?
 Closeness Centrality—The reciprocal of the sum of the shortest path distances of a node to all other nodes in the network. A node with the highest closeness centrality score has the shortest average path to all other nodes in the network. Closeness centrality should be used when determining which nodes are most closely associated to the other nodes in the network.
Closeness centrality can help answer the following questions:
 In an epidemic, who was most likely patient zero?
 Who would be the best person or brand to get the most people informed about a product on social media?
 Degree Centrality—The total number of immediate connections a given node has in the network. These connections can further be divided into incoming or outgoing connections to better understand the nature of the node's relationships to its neighbors. Degree centrality allows for the determination of which nodes have the most direct influence in the network. Directed networks will further include In Degree and Out Degree Centrality. These represent how many incoming and outgoing connections a given node has.
Degree centrality can help answer the following questions:
 Who are the largest influencers on a social media platform?
 Which airport connects to the largest number of destinations?
In Degree centrality can help answer the following questions:
 Who is followed by the most people on a given social media platform?
 Which airport has the most flights arriving to it from other airports?
Out Degree centrality can help answer the following questions:
 Who is following the most people on a given social media platform?
 Which airport connects to the most destinations?
 Eigenvector Centrality—Eigenvector centrality is based on important nodes being connected to other important nodes. Eigenvector centrality allows for the determination of whether there are clusters of influence in a given network. Eigenvector works when all relationships in the network are bidirectional or directionality is annotated on the network.
Eigenvector centrality can help answer the following questions:
 Are there groups of highly influential people on a given social media platform? And who are they?
 PageRank Centrality—PageRank centrality is similar to Eigenvector centrality in that it is suited best to measuring influence in a network. However, while Eigenvector does not take directionality into account, PageRank does. PageRank centrality was designed and popularized by Google to help in returning the most optimal search result to a user's question.
PageRank centrality can help answer the following questions:
 Which website best answers my question?
 Which node in my IT network is the most critical?
 Who in my criminal network exerts the most influence over the entirety of the network?
The analysis runs and the tabular results appear in the analysis window.
 Betweenness Centrality—How often a node lies on the shortest path between each pair of nodes in the network. Removing or adding a central node may result in the shortest paths to change, necessitating recalculation of the betweenness centrality. Betweenness centrality allows for the identification of nodes that act as connecting nodes between nodes in a network.