--

input.py

@@ -59,8 +59,7 @@ def mask_test_edges(adj):
     # edges_all相当于排列，就都包含了
     edges = adj_tuple[0]
     edges_all = sparse_to_tuple(adj)[0]
-    # 取edge的10%作为test
-    # 取edge的20%作为val
+
     num_test = int(np.floor(edges.shape[0] / 10.))
     num_val = int(np.floor(edges.shape[0] / 20.))
 

train.py

@@ -46,7 +46,7 @@ if __name__ == "__main__":
     feature_sample = pca.fit_transform(feature_sample)
     kde = KernelDensity(bandwidth=0.7).fit(np.array([feature_sample[i] for i in index]))
 
-    # 计算图中预输入数据
+    # 预输入的数据
     placeholders = {
         'features': tf.sparse_placeholder(tf.float32),
         'features_dense': tf.placeholder(tf.float32, shape=[feas['adj'].shape[0], feas['num_features']], name='real_distribution'),