The DOI matches its Crossref record; 55 of the 55 references it cites were found in a registry or an index.
A new method for constructing networks from binary data
Open accessAbstract
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.
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A network theory of mental disorders
Borsboom, D. (2017) · World Psychiatry · doi:10.1002/wps.20375 · Cited by 3,482
Self-citationA tutorial on regularized partial correlation networks
Epskamp, S., & Fried, E. I. (2018) · Psychological Methods · doi:10.1037/met0000167 · Cited by 2,918
Self-citationNetwork analysis of multivariate data in psychological science
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Self-citationMoving Forward: Challenges and Directions for Psychopathological Network Theory and Methodology
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Metadata
- Authors
- van Borkulo, C. D., Borsboom, D., Epskamp, S., Blanken, T. F., Boschloo, L., Schoevers, R. A., & Waldorp, L. J.
- Venue
- Scientific Reports
- Published
- 2014-08-01
- Volume
- 4
- Issue
- 1
- Pages
- 5918-5918
- ISSN
- 2045-2322 (ISSN-L)
- OpenAlex
- W2015530535
- Cited by
- 757
- References
- 55
- FWCI
- 38.59
- Type
- journal article
- Language
- en
- Open access
- gold
- Licence
- creativecommons.org/licenses/by-nc-nd/4.0 (tdm)
- creativecommons.org/licenses/by-nc-nd/4.0 (vor)
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- van Borkulo, C. D., Borsboom, D., Epskamp, S., Blanken, T. F., Boschloo, L., Schoevers, R. A., & Waldorp, L. J. (2014). A new method for constructing networks from binary data. Scientific Reports. https://doi.org/10.1038/srep05918
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
