debug

evalNE_script.py

@@ -64,7 +64,7 @@ methods = ['random_prediction',
 pbar = tqdm(methods,disable= (not args.verbose))
 for method in pbar:
     pbar.set_description("Evaluate "+method)
-    result = nee.evaluate_baseline(method=method, )
+    result = nee.evaluate_baseline(method=method,)
     scoresheet.log_results(result)
 
 if args.network_embedding:
@@ -91,7 +91,7 @@ if args.network_embedding:
             pbar.set_description("Evaluate "+method)
             command = commands[i] + " --input {} --output {} --representation-size {}"
             results = nee.evaluate_cmd(method_name=methods[i], method_type='ne', command=command,
-                                       edge_embedding_methods=edge_emb, input_delim=' ', output_delim=' ',  verbose=args.verbose)
+                                       edge_embedding_methods=edge_emb, input_delim=' ', output_delim=' ',  verbose=args.verbose,write_weights=nx.is_weighted(G))
             scoresheet.log_results(results)
 
     except ImportError:

lib/random.py

@@ -103,7 +103,8 @@ def _conf_model(degree_seq):
 
 def powerlaw_graph(nb_nodes, nb_edges, exponent=2, tries=1000, min_deg=1):
     """
-    Generate a graph with a definied number of vertices, edges, and a degree distribution that fit the power law.
+    Generate a graph with a defined number of vertices, edges, and a degree distribution that fit the power law.
+    Using the Molloy-Reed algorithm to
     Parameters
     ----------
     nb_nodes : int