Networks of Symptoms and Exposures
Bibliographic Data
| ID | 3099609 |
|---|---|
| Authors | Andrew J Seary, Andrew Seary, William D Richards (0000-0002-2042-5124), Gail E Mckeown-Eyssen, Gail Mckeown‐eyssen, Cornelia J Baines, Cornelia Baines |
| Year | 2005 |
| Volume | 1 |
| Issue | 2 |
| Publication date | 2005-01-01 |
| Open Access | No |
| Type | ARTICLE |
| Venue | Structure and Dynamics eJournal of Anthropological and Related Sciences (JOURNAL) |
| Journal identifiers | ISSN: 1554-3374 • E-ISSN: 1554-3374 |
| Publisher | California Digital Library (CDL) (PUBLISHER) |
| DOI | 10.5070/sd912003276 |
| OpenAlex | W1567694162 |
| Language | EN |
| References cited | 3 |
We present some novel methods for analyzing and visualizing data from medical studies using methods originally developed for the study of social networks. The methods are based on spectral (eigendecomposition) properties of networks, in particular the so-called Normal spectrum. Among the many desirable properties of this spectrum is the natural handling of bipartite (2-mode) networks through negative eigenvalues, the clustering properties related to positive eigenvalues, and the relationship to the chi-squared measure of dependence in contingency tables
Bipartite graph · Cluster analysis · Contingency table · Data mining · Eigendecomposition of a matrix · Eigenvalues and eigenvectors · Measure (data warehouse · Pattern recognition (psychology · Physics · Spectral clustering · Spectral properties · Spectrum (functional analysis · Statistics · Complex Network Analysis Techniques · Computer Science · Mathematics · Mental Health Research Topics · Topological and Geometric Data Analysis · Artificial Intelligence · Theoretical Computer Science
| Highly cited | No |
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