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**NETWORK > CENTRALITY > TOTAL CENTRALITY**

**PURPOSE **Calculates the total, endogenous and exogenous centrality scores together with the dyadic dependency matrix for degree, betweenness, reverse closeness and k-step reach centrality.

**DESCRIPTION **Given a set of centrality scores the total sum of all the scores is a graph invariant. A graph invariant can be used to induce a centrality score by measuring the contribution each node makes to the invariant. The induced centrality of the total sum of centrality scores is the total centrality of a node. The original centrality score is called the endogenous centrality and the total centrality minus this score is the exogenous centrality. The dyadic dependency matrix is a node-by-node matrix P in which pij gives the difference between j's total centrality when i is present and j's total centrality when i is absent.

**PARAMETERS**

**Input network:**

** **Name of file containing network to be analyzed. Data type: Digraph

**Output Measures:** (Default = <filename>-tcd).

Name of file which will contain total, endogenous and exogenous centrality scores for each vertex.

**(Ouput) Dyadic contributions: **(Default=<filename>-tcdd)

Name of file which will contain the dyadic dependency matrix.

**For direcetd data use**

** **Radio button that allows the user to select the in or out version of a directed centrality measure to be used as the base endogenous centrality.

**Base measure**

** **Measure to be used as the underlying endogenous centrality. Choices are degree, reverse closeness, betweenness and k-step reach centrality.

**LOG FILE **A table of the total, endogenous and exogenous centrality scores for each actor followed by the dyadic dependency matrix.

**TIMING** O(N^4).

**COMMENTS **

**REFERENCES** Everett M G and S P Borgati 2010. Induced, endogenous and exogenous centrality. Social Networks