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: 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))
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?
Is there also any way to get the full posterior of each node belonging to each block?
Thanks in advance.