add ablation study and synthetic dataset generation

can.py

@@ -204,7 +204,7 @@ def training(args):
         end_time = time.time()
         print("total time spend:", end_time- start_time)
 
-        pre, mu_c=clustering_latent_space(z.cpu().detach().numpy(),tru)
+        pre, mu_c = clustering_latent_space(z.cpu().detach().numpy(),tru)
         plot_tsne(args.dataset,args.model,epoch,z.cpu(),torch.tensor(mu_c),Y,pre)
 
         print("label mapping using Hungarian algorithm ")

evaluation.py

@@ -14,7 +14,7 @@ def clustering_latent_space(emb, label, nb_clusters=None):
     if nb_clusters is None:
         nb_clusters = len(np.unique(label))
     # K-Means Clustering
-    km = KMeans(n_clusters = nb_clusters,init='random')
+    km = KMeans(n_clusters = nb_clusters,max_iter=500)
     clustering_pred = km.fit_predict(emb)
     # Compute metrics
     return clustering_pred, km.cluster_centers_

generate_synthtic_graph.py

@@ -0,0 +1,74 @@
+import networkx as nx
+import numpy as np
+from networkx.generators import erdos_renyi_graph
+
+attribute_size = 1000
+
+def generate_graphs(num_nodes, density):
+
+    G = erdos_renyi_graph(num_nodes,density)
+
+    # features for each node
+
+    fea_matrix = np.random.choice(attribute_size,(num_nodes,10))
+
+
+    # labels
+    label = np.random.choice(6,num_nodes)
+
+
+    return G, fea_matrix, label
+
+def save_graph(num_nodes,density,G,fea_matrix,label):
+
+    #save edges
+    wp = open('synthetic_{}_{}.edge'.format(num_nodes,density),'w')
+
+    wp.write("#Nodes {}\n".format(num_nodes))
+    wp.write("#Edges {}\n".format(len(G.edges)))
+
+    for e in G.edges:
+        wp.write("{}\t{}\n".format(e[0],e[1]))
+
+    wp.close()
+
+    #save node
+
+    wp = open('synthetic_{}_{}.node'.format(num_nodes,density),'w')
+
+    wp.write("#Nodes {}\n".format(num_nodes))
+    wp.write("#Attributes {}\n".format(attribute_size))
+
+    for n in range(num_nodes):
+        for attr in fea_matrix[n]:
+            wp.write("{}\t{}\n".format(n,attr))
+    wp.close()
+
+    #save label
+
+    wp = open('synthetic_{}_{}.label'.format(num_nodes,density),'w')
+
+    for n in label:
+        wp.write("{}\n".format(n))
+
+    wp.close()
+
+
+if __name__=="__main__":
+    num_nodes = 1000
+    density = 0.1
+    G,fea_matrix,label = generate_graphs(num_nodes,density)
+    save_graph(num_nodes,density,G,fea_matrix,label)
+    print("Done")
+
+
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