  Network models, in which psychopathological disorders are conceptualized as a
complex interplay of psychological and biological components, have become
increasingly popular in the recent psychopathological literature. These network
models often contain significant numbers of unknown parameters, yet the sample
sizes available in psychological research are limited. As such, general
assumptions about the true network are introduced to reduce the number of free
parameters. Incorporating these assumptions, however, means that the resulting
network will lead to reflect the particular structure assumed by the estimation
method---a crucial and often ignored aspect of psychopathological networks. For
example, observing a sparse structure and simultaneously assuming a sparse
structure does not imply that the true model is, in fact, sparse. To illustrate
this point, we discuss recent literature and show the effect of the assumption
of sparsity in three simulation studies.
