(Mis)interpreting Networks: An Abbreviated Tutorial on Visualizations

This guest post was written by Payton Jones (payton_jones@g.harvard.edu), and is the abbreviated version of a tutorial published in Frontiers recently (“Visualizing Psychological Networks: A Tutorial in R“). Payton is a graduate student at Harvard University in the Richard J. McNally lab. His research focuses on the etiology of mental …

How to interpret centrality values in network structures (not)

For posts on psych-networks, I usually want to write about a topic for a few months, don’t find the time, and then a new paper comes along that prompts me to write up what I had in mind. For instance: A paper by Madhoo & Levin 2016 prompted me to …

Expected influence: a new centrality metric by Robinaugh et al. 2016

When we estimate the centrality of nodes in networks, one metric that is commonly used is node strength centrality or degree centrality. In unweighted networks were edges are either 0 or 1, this is simply the sum of edges. Let’s create a brief example in R: we create an adjacency …

2 new conceptual network papers from a clinical perspective

Two new conceptual papers were published recently that tackle the question what network theory and network models can contribute to clinical science. Together, they provide a great introductory read and cover a large number of clinically relevant topics. While many technical papers are available by now, it is good to …