SachaEpskamp
commented
2 years ago Hi Matthew,
I have no plans of adding this in the near future. Mainly, I find weighted variants of transitivity and clustering quite troublesome. For unweighted networks this is clear: are neighbors of a node also each-others neighbors. But for weighted networks this is not clear anymore, and there are many different interpretations you can take. To this end, I did not implement such variants back when I was actively developing qgraph, and I don't think I will implement them soon,
Best, Sacha
mvanaman
Hi Dr. Epskamp,
This is not an "issue" per se, but seems potentially relevant to future implementations of qgraph.
I am working through some of your published tutorials in the qgraph package (all of which are great by the way!). In Constantini et al. (1), network transitivity is introduced in a way that does not take into account weights and signs, but then mentions that Opsahl & Panzarasa (2) and Kunegis et al. (3) have found solutions that do take those into account. In the qgraph documentation, I see that transitivity (called by the
smallworldness()function) also does not take into account weights and signs. I also looked at thetransitivity()function from the igraph package, but this too seems to ignore weights and signs (for global transitivity - local transitivity i.e., for individual nodes does seem to take those into account).I was wondering if you knew of an implementation in r for getting the weighted and signed transitivity, and/or if you guys were planning on implementing/would consider implementing this into future iterations of qgraph?
Thanks for your time, Matthew
(1) Costantini, G., Epskamp, S., Borsboom, D., Perugini, M., Mõttus, R., Waldorp, L. J., & Cramer, A. O. (2015). State of the aRt personality research: A tutorial on network analysis of personality data in R. Journal of Research in Personality, 54, 13-29.
(2) Opsahl, T., & Panzarasa, P. (2009). Clustering in weighted networks. Social Networks, 31(2), 155–163. http://dx.doi.org/10.1016/j.socnet.2009.02.002.
(3) Kunegis, J., Lommatzsch, A., & Bauckhage, C. (2009). The Slashdot Zoo: Mining a social network with negative edges. In Proceedings of the 18th international conference on world wide web (pp. 741–750). http://dx.doi.org/10.1145/ 1526709.1526809.