Sir, I am trying to follow the example on "edge prediction as binary classification". Here is my code: *import graph_tool as gt import pandas as pd* # create a graph object in data frame format *ndf = pd.DataFrame({'Node1':['a','b','c','d','e'],'Node2':['c','e','b','a','d'],'Weight':[0.2,0.8,0.4,0.5,0.7], 'RandProp1':[1,2,3,1,2]}) ng = gt.Graph() nprop = ng.new_edge_property("float") ng.edge_properties['Weight'] = nprop* # important to map the properties to the graph *LayerProp = ng.new_edge_property('float') ng.edge_properties['LayerProp'] = LayerProp* *nvp = ng.add_edge_list(ndf.values.tolist(),hashed=True,string_vals=True,eprops=[nprop,LayerProp])* # minimizing the graph and inferring partitions *stateA = gt.inference.minimize_nested_blockmodel_dl(ng,layers=True, state_args=dict(ec=LayerProp,layers=True), deg_corr=True,verbose=True)* *L = 10 bs = stateA.get_bs() bs += [np.zeros(1)]*(L-len(bs)) stateB = stateA.copy(bs=bs, sampling=True) probs=([]) def collect_edge_probs(s): p = s.get_edges_prob([missing_edges[0]],entropy_args=dict(partition_dl=False)) probs[0].append(p); missing_edges = [(1,2,1)] *# for layered network you need to specify layer number *gt.inference.mcmc_equilibrate(stateB,force_niter=1000,mcmc_args=dict(niter=10), callback=collect_edge_probs,verbose=True)* When I run this code, it gives me kernel died error. Please help. I have another query that; how can we get the layer associated with the node? In the above code when I try the command *for i in nvp: print(i)* I get the output as : *a,c,b,e,d* and when I type the command *LayerProp.a* I get the output: *PropertyArray([ 1., 2., 3., 1., 2.])* How do I understand that because the order of addition of nodes depends on the order they come along with add_edge_list command, while the LayerProp is added in the order as mentioned in the property map. -- Sent from: http://main-discussion-list-for-the-graph-tool-project.982480.n3.nabble.com/