Networks: On the relation of bi- and multivariate measures

Wolfgang Mader, Malenka Mader, Jens Timmer, Marco Thiel, Bjoern Schelter

Research output: Contribution to journalArticlepeer-review

13 Citations (Scopus)
11 Downloads (Pure)


A reliable inference of networks from observations of the nodes’ dynamics is a major challenge in physics. Interdependence measures such as a the correlation coefficient or more advanced methods based on, e.g., analytic phases of signals are employed. For several of these interdependence measures, multivariate counterparts exist that promise to enable distinguishing direct and indirect connections. Here, we demonstrate analytically how bivariate measures relate to the respective multivariate ones; this knowledge will in turn be used to demonstrate the implications of thresholded bivariate measures for network inference. Particularly, we show, that random networks are falsely identified as small-world networks if observations thereof are treated by bivariate methods. We will employ the correlation coefficient as an example for such an interdependence measure. The results can be readily transferred to all interdependence measures partializing for information of thirds in their multivariate counterparts.
Original languageEnglish
Article number10805
Number of pages7
JournalScientific Reports
Early online date4 Jun 2015
Publication statusPublished - 4 Jun 2015

Bibliographical note

Date of Acceptance: 28/04/2015
The article processing charge was funded by the German Research Foundation (DFG) and the Albert Ludwigs University Freiburg in the funding programme Open Access Publishing


Dive into the research topics of 'Networks: On the relation of bi- and multivariate measures'. Together they form a unique fingerprint.

Cite this