A new method for constructing networks from binary data.

ABSTRACT: Network analysis is entering fields where network structures are unknown, such as psychology and the educational sciences. A crucial step in the application of network models lies in the assessment of network structure. Current methods either have serious drawbacks or are only suitable for Gaussian data. In the present paper, we present a method for assessing network structures from binary data. Although models for binary data are infamous for their computational intractability, we present a computationally efficient model for estimating network structures. The approach, which is based on Ising models as used in physics, combines logistic regression with model selection based on a Goodness-of-Fit measure to identify relevant relationships between variables that define connections in a netw

SUBMITTER: van Borkulo CD 

PROVIDER: S-EPMC4118196 | biostudies-literature | 2014 Aug

REPOSITORIES: biostudies-literature