A new method for constructing networks from binary data

• Published in: Scientific reports Vol. 4; no. 1; p. 5918
• Main Authors: van Borkulo, Claudia D., Borsboom, Denny, Epskamp, Sacha, Blanken, Tessa F., Boschloo, Lynn, Schoevers, Robert A., Waldorp, Lourens J.
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 01.08.2014; Nature Publishing Group
• Subjects: 631/477; 639/705; 692/1807; Algorithms; Anxiety; Approximation; Case-Control Studies; Computer applications; Computer Simulation; Data processing; Depression - diagnosis; Depression - psychology; Humanities and Social Sciences; Humans; Mental depression; Methods; Models, Theoretical; multidisciplinary; Psychology; Psychopathology; Regression analysis; Science; Software; Validation studies; Variables
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/srep05918

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 network. A validation study shows that this method succeeds in revealing the most relevant features of a network for realistic sample sizes. We apply our proposed method to estimate the network of depression and anxiety symptoms from symptom scores of 1108 subjects. Possible extensions of the model are discussed.