Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
1129558 | Social Networks | 2006 | 21 Pages |
Abstract
Ignoring the multiplex nature of social networks might result in sets of empirical findings that are misleading or contradictory. It would be helpful to adapt a variety of multivariate statistical techniques to handle network-oriented data. A canonical correlation analysis (CCA) makes a particularly interesting example since it is the most general form of the general linear model. In an example involving 317 banks, a CCA is applied to two multiplex networks: interdependence and cooperative alliances. Four significant patterns of association (orthogonal linear functions) are identified.
Related Topics
Physical Sciences and Engineering
Mathematics
Statistics and Probability
Authors
Charles Carroll,