Impact is a new statistic for network analysis. Like centrality, it aims to answer the question of “which nodes are important”. Impact asks this question in a slightly different way: it measures the degree to which nodes impact network structure.
The visual presentation of networks can occasionally be misleading. For instance, researchers may be tempted to conclude that nodes which are close together are highly related, and nodes that are far apart are not. In the dominant plotting approach, the Fruchterman-Reingold algorithm, this is not the case. There exist many other methods for plotting networks which may be more appropriate and readily interpretable. We provide a brief tutorial on several methods including multidimensional scaling, principal components plotting, and eigenmodel networks. These methods may be especially appropriate for those who wish to visually compare replications of network analyses.
The objective of this document is to provide a collection of scientific papers (original research articles, editorials and preprints) that relate to network analysis in eating disorders research, including summaries of findings.
The objective of this document is to provide a collection of articles, tutorials, and software for methods for psychometric network analysis.
The objective of this document is to provide a collection of scientific papers (original research articles, editorials and preprints) that relate to the network theory of mental disorders.