An analysis is conducted on the robustness of measures of centrality in the face of random error in the network data. We use random networks of varying sizes and densities and subject them (separately) to four kinds of random error in varying amounts. The types of error are edge deletion, node deletion, edge addition, and node addition. The results show that the accuracy of centrality measures declines smoothly and predictably with the amount of error. This suggests that, for random networks and random error, we shall be able to construct confidence intervals around centrality scores. In addition, centrality measures were highly similar in their response to error. Dense networks were the most robust in the face of all kinds of error except edge deletion. For edge deletion, sparse networks were more accurately measured.
|Number of pages||13|
|State||Published - May 2006|
Bibliographical noteFunding Information:
This paper is part of the Dynamics Networks project in CASOS at CMU. This work was supported in part by the Department of Defense, the Office of Naval Research under grant no. 9620.1.1140071 on Dynamic Network Analysis, DARPA DAAH01-03-C-R111, and the National Science Foundation under MKIDS and the CASOS IGERT program. Additional support was provided by CASOS—the center for Computational Analysis of Social and Organizational Systems at Carnegie Mellon University ( http://www.casos.ece.cmu.edu ). The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the Department of Defense, the Office of Naval Research, Darpa, the National Science Foundation or the U.S. government.
ASJC Scopus subject areas
- Sociology and Political Science
- Social Sciences (all)
- Psychology (all)