The recent study on dynamical spread of happiness in social networks uses the Eigen Value Centrality. I would like to quote the relevant portion from the paper
Figure 1 also suggests a relation between network centrality and happiness: people at the core of their local networks seem more likely to be happy, while those on the periphery seem more likely to be unhappy. We tested this by computing eigenvector centrality measures for each subject. Generalised estimating equation regressions show that ego centrality is significantly associated with improved future happiness: a 2 SD increase in centrality (from low to medium or medium to high) increases the probability of being happy at the next examination by 14% (1% to 29%, P=0.03). Moreover, the relation between centrality and future happiness remained significant even when we controlled for age, education, and the total number of family and non-family alters. Thus, it is not only the number of direct ties (at one degree of separation) but also the number of indirect ties (at higher degrees of separation) that influence future happiness. The better connected are one’s friends and family, the more likely one will attain happiness in the future. Conversely, happiness itself does not increase a person’s centrality at subsequent time points (see appendix on bmj.com). That is, network centrality leads to happiness rather than the other way around.
Interestingly I have used eigen value centrality to computer system complexity and distribution of complexity in large scale software systems ( Please see a case study published in TRIZ JOURNAL). This framework called System Complexity Estimator and System Change Impact Model (SCE-SCIM) has proved extremely potent.
Unfortunately the eigen value centrality is not easily understood by business world - Well if you need to know - send me an email ... I shall be able to help you in applying this for various applications!