Most empirical networks are ambiguous in the middle, which is on the one hand quite obvious (it's difficult to attribute those nodes to a single cluster) and on the other hand constantly overlooked in network interpretation (the clusters found by Louvain are taken too seriously for those nodes).

This opens a new tab where you will see your network with the ambiguous nodes and edges featured in white.
(Read the paper for more info on this!)

3) Click on the "Compute metrics" button.

4) Scroll the panel down, and click on the "Observe ambiguity visually" button at the bottom of the panel.

To try the feature in Gephi Lite, do this:

1) Open Gephi Lite and a network of your choice. I will use the "divided they blog" network because it is simple.

2) In the side bar on the left, click on Metrics > Louvain edges ambiguity (that's the last item).

Our case is the ambiguity of categories obtained by community detection algorithms in networks.

We offer an implementation of our ambiguity visualization method in Gephi Lite, which you can try online right now!

lite.gephi.org

"A blurry picture of a sharp thing must not be confused with a sharp picture of a blurry thing."

Our paper defends ambiguity as substantive knowledge to visualize. Ambiguity is often a defining feature of the phenomena we study, and should therefore be measured. It is not the same as uncertainty, which we want to mitigate.

anthology.ach.org/volumes/vol0...

Kudos to my own co-authors, Tommaso and @bndndr.it from Density Design (at the time of writing the paper), @yomguithereal.bsky.social and @boogheta.bsky.social from the @medialab-scpo.bsky.social, and Paul and Alexis (my brother) from @ouestware.bsky.social .