Dear list, in the example on Edge layers and covariates <https://graph-tool.skewed.de/static/doc/demos/inference/inference.html#edge-layers-and-covariates>, blocks are fitted as follows: state = gt.minimize_blockmodel_dl(g, deg_corr=False, layers=True, state_args=dict(ec=g.ep.value, layers=False)) I'm trying to make sure I understand the LayeredBlockState correctly. Are the following statements correct? 1. The independent layers version is used, which means that there is one layer for every possible number of co-appearances. *This means that number of co-appearances is treated as a categorical, rather than an ordinal variable. * 2. If one wanted to encourage the model to assort actors into the same block if they have many co-appearances, the following fit would be more appropriate: state = gt.minimize_blockmodel_dl(g, deg_corr=False, layers=False, state_args=dict(eweight=g.ep.value)) (If I'm right, then I find that the second model is closer to what an applied scientist would be interested in...) Many thanks for clearing this up, Peter -- Dr Peter Straka Research Fellow (DECRA) Dep. of Statistics | School of Mathematics & Statistics | UNSW Australia Google Scholar <https://scholar.google.com.au/citations?user=BV5PkWUAAAAJ&hl=en&authuser=1> E: p.straka@unsw.edu.au T: +61 (2) 938*5 7024 *| +1 313 757 0137