Correlation Graphs

[BradKML]

R has default libraries for Correlation Networks

• https://stackoverflow.com/a/12374265
• https://www.r-graph-gallery.com/250-correlation-network-with-igraph.html
• https://psych-networks.com/r-tutorial-identify-communities-items-networks/

It is rather hard to see how strong the ties should be for Python, especially if the ties are negatively correlated.

[BradKML]

https://towardsdatascience.com/eveything-you-need-to-know-about-interpreting-correlations-2c485841c0b8

On this page, anything under 0.3 makes negligible correlation, and anything over 0.9 is clearly correlated, with 0.6 being the middle of correlation significance. Possibly, for scaling purposes (where 0 correlation leads to 0 weight, and 0.6 correlation leads to 1, 1 correlated to b^0.583):

from numpy import tanh, exp
def scale(x):
    return exp(tanh((x-0.6)/0.6))

[jhunpingco]

I'm going to mark this as out of scope for Pyvis. Closed.