Hi there,

I am trying to include the edge weights by taking to account an edge covariate matrix for the nested block model inference. Well, Each time I run the code on my data set I get slightly different results both in terms of number of blocks and the nodes in each block.

This is my code:I appreciate if you explain what your approach would be and how I can run graph-tool using the covariance matrix of edges in order to get statistically reliable results?

state = minimize_nested_blockmodel_dl(g, state_args=dict(recs=[g.edge_properties["weight"]], rec_types=["discrete-geometric"])) state.draw(edge_color=prop_to_size(g.edge_properties["weight"], power=1, log=True), ecmap=(matplotlib.cm.gist_heat, .6), eorder=g.edge_properties["weight"], edge_pen_width=prop_to_size(g.edge_properties["weight"], 1, 4, power=1, log=True), edge_gradient=[], vertex_text=g.vertex_properties["attribute"], vertex_text_position="centered", vertex_text_rotation=g.vertex_properties['text_rotation'], vertex_font_size=10, vertex_font_family='mono', vertex_anchor=0, output_size=[1024*2,1024*2], output="DiscreteGeometric_%s.pdf"%(eventName))

Is there also any way to get the full posterior of each node belonging to each block?