Seurat clustering Methods-resolution parameter explanation

I am learning the Seurat algorithms to cluster the scRNA-seq datasets. I found this explanation, but am confused. Can someone explain it to me, "The FindClusters function implements the procedure, and contains a resolution parameter that sets the ‘granularity’ of the downstream clustering, with increased values leading to a greater number of clusters. We find that setting this parameter between 0.6-1.2 typically returns good results for single cell datasets of around 3K cells". My questions are:

1. What is "granularity" of the downstream analysis?
2. How does the parameter "resolution" work?
3. How to know the optimum value for parameter "resolution"?

I found this paper about the resolution that might help, but not clear to me yet. https://iopscience.iop.org/article/10.1088/1742-5468/2008/10/P10008/pdf.

I think the higher the resolution, the more clusters produced meaning the smaller clusters obtained? The smaller the clusters the more specific cell types we get, is that correct?

What is "granularity" of the downstream analysis?

Low granularity implies basic cell types (e.g. T cells, B cells), implies few larger clusters.

High granularity implies more refined cell types (e.g. CD4+ Central Memory T, Treg, Memory B cell etc.), implies more smaller clusters

How does the parameter "resolution" work?

A high resolution parameter value produces more smaller clusters, i.e. higher granularity. If you want to know how modularity clustering works, I recommend the Leiden algorithm paper https://www.nature.com/articles/s41598-019-41695-z

How to know the optimum value for parameter "resolution"?

Tricky, and it depends on what questions you want to answer. Normally it's a subjective iterative process of trying a clustering resolution, checking if the clusters make sense, re-trying etc. but I also quite like the clustree approach https://cran.r-project.org/web/packages/clustree/index.html

The smaller the clusters the more specific cell types we get, is that correct?

Yes correct