Networks have emerged as a popular method for studying mental disorders.
Psychopathology networks consist of aspects (e.g., symptoms) of mental disorders
(nodes) and the connections between those aspects (edges). Unfortunately, the visual
presentation of networks can occasionally be misleading. For instance, researchers
may be tempted to conclude that nodes that appear close together are highly related,
and that nodes that are far apart are less related. Yet this is not always the case. In
networks plotted with force-directed algorithms, the most popular approach, the spatial
arrangement of nodes is not easily interpretable. However, other plotting approaches
can render node positioning interpretable. We provide a brief tutorial on several methods
including multidimensional scaling, principal components plotting, and eigenmodel
networks. We compare the strengths and weaknesses of each method, noting how to
properly interpret each type of plotting approach.
Keywords: network analysis, network psychometrics, psychopathology, multidimensional scaling, graph theory
The published version of this manuscript is available (open access) at https://www.frontiersin.org/articles/10.3389/fpsyg.2018.01742/full
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