A new method for constructing networks from binary data

By Claudia D van Borkulo, Denny Borsboom, Sacha Epskamp, T. Blanken, L. Boschloo, Robert A. Schoevers, Lourens J. Waldorp. Scientific Reports, 2014

674 citations recorded by the source corpus.

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.

Subfields: Cognitive Neuroscience, Experimental and Cognitive Psychology, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/2b005c66432ae438ebb0e6a00dc54cf3d724d9bf

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Citations in the corpus

Comparing network structures on three aspects: A permutation test. (2022, methodology): “The network estimation procedure we used to estimate Ising models was IsingFit (Van Borkulo et al., 2014).”
The Gaussian Graphical Model in Cross-Sectional and Time-Series Data (2016, methodology): “The performance of pooled and individual LASSO estimation is assessed in simulations reported in Section 3 of the supplementary materials.”
Exploratory graph analysis: A new approach for estimating the number of dimensions in psychological research (2016, methodology): “This issue can be solved using regularization techniques, such as the LASSO [43], which is one of the most prominent methods for network estimation on psychological datasets [38, 40, 44].”
Network analysis: a brief overview and tutorial (2018, methodology): “If all the research variables are binary, the Ising Model can be used (van Borkulo et al., 2014).”
What do centrality measures measure in psychological networks? (2019, background): “…are partial correlations for cross-sectional data (i.e., cross-sectional network; Costantini et al., 2015; Epskamp, Waldorp, Mõttus, & Borsboom, 2018; van Borkulo et al., 2014) and vector autoregressive based models for intensive longitudinal data (i.e., temporal network; Bringmann et al., 2013).”
Regularized Gaussian Psychological Networks: Brief Report on the Performance of Extended BIC Model Selection (2016, methodology): “When variables are binary, a more appropriate model to use is the Ising Model (van Borkulo et al., 2014).”
Generalized Network Psychometrics: Combining Network and Latent Variable Models (2016, background): “As is typical in simulation studies investigating the performance of network estimation techniques, we investigated the sensitivity and specificity (van Borkulo et al., 2014).”
Estimating psychological networks and their accuracy: A tutorial paper (2016, methodology): “When the data are binary, the appropriate PRMF model to use is called the Ising model (van Borkulo et al., 2014), and requires binary data to be estimated.”