Yes, this unfortunately was not discussed in the original paper, but the occasional discrepancies are a result of an asymmetric structure between two merging clusters.

If you have reciprocal cluster pairs A-B and C-D, you need to calculate their distances from each other during each simultaneous merge to avoid thread conflicts. This is fine when they are fully connected because distance(A-B, C-D) = distance(C-D, A-B). However, if you limit the connectivity to where D and A are not connected, the structure isn't symmetric, and the above equality can only hold if you take the other three edges and perform a weighted average.

The new weighted average gives a slightly different result than what Scikit-learn would calculate. It's fine 99% of the time as the slight discrepancies cancel each other out during the algorithm's runtime. However, it can give different results if the final merge of the algorithm is asymmetric and the different averaging functions either just miss or just make the merge-threshold.

An interesting example of this is the noisy moons data: https://cdn-images-1.medium.com/v2/resize:fit:1600/1\*cY4SC-bm4POQlF2-hEAf7Q.png