@apajournals.bsky.social

The method works both for panel and n=1 data. By enabling researchers to statistically compare networks across groups/individuals, we hope the method opens new avenues for testing genetic influences, developmental theories, treatment mechanisms, and cross-cultural differences.

I planned to present this method at #SAA2025. Unfortunately I could not make it due to an unforeseen cold. Hope you enjoy the discussion and stay safe and healthy!

The paper also comes with a brief tutorial on the usage of the package

We have implemented IVPP in an R package under the same name: github.com/xinkaidupsy/...

Second, the method allows the comparison of networks when only a few data points (t = 3 or more) are available per person, a situation that is very common in large-scale longitudinal surveys.

In contrast, IVPP uncovers edge-level differences through a novel algorithm we present, termed partial pruning, directly constructing the distinct networks of each group/individual. We believe it provides a more meaningful network difference test that reveals the mechanisms underlying heterogeneity.

IVPP fills in two essential gaps in the literature: First, previous approaches to comparing dynamical networks unfortunately only report the presence/absence of heterogeneity, and are only viable when intensive measurements are available.

Thank the collaborators for the continuous support and contribution! @noraskjerdingstad.bsky.social @renefreichel.bsky.social @omidvebrahimi.bsky.social Ria Hoekstra @sachaepskamp.bsky.social

The Shiny app allows users to view the results interactively, as well as checking the rejection rates of different cutoff values they choose by themselves

2. Fit indices were sensitive to mis-defined confirmatory network structures and non-stationarity.
3. Conventional cutoffs were convenient assessment criteria and generally performed well, albeit stricter cutoffs might be needed for hypothesis testing and replication studies

1. Although most network studies are exploratory so far, confirmatory network analysis has been entirely feasible. It is also often neglected that in longitudinal settings, exploratory network models are in-fact semi-confirmatory for the stationarity assumption they rely on.