[Bug Fix] Fix the case when reverse_edge is False for citation graphs (#3840)

* Update citation_graph.py

* Update

* Update

* Update
Co-authored-by: Minjie Wang <wmjlyjemaine@gmail.com>

python/dgl/data/citation_graph.py

@@ -122,8 +122,12 @@ class CitationGraphDataset(DGLBuiltinDataset):
 
         if self.reverse_edge:
             graph = nx.DiGraph(nx.from_dict_of_lists(graph))
+            g = from_networkx(graph)
         else:
             graph = nx.Graph(nx.from_dict_of_lists(graph))
+            edges = list(graph.edges())
+            u, v = map(list, zip(*edges))
+            g = dgl_graph((u, v))
 
         onehot_labels = np.vstack((ally, ty))
         onehot_labels[test_idx_reorder, :] = onehot_labels[test_idx_range, :]
@@ -137,9 +141,6 @@ class CitationGraphDataset(DGLBuiltinDataset):
         val_mask = generate_mask_tensor(_sample_mask(idx_val, labels.shape[0]))
         test_mask = generate_mask_tensor(_sample_mask(idx_test, labels.shape[0]))
 
-        self._graph = graph
-        g = from_networkx(graph)
-
         g.ndata['train_mask'] = train_mask
         g.ndata['val_mask'] = val_mask
         g.ndata['test_mask'] = test_mask
@@ -204,7 +205,6 @@ class CitationGraphDataset(DGLBuiltinDataset):
         graph.ndata.pop('feat')
         graph.ndata.pop('label')
         graph = to_networkx(graph)
-        self._graph = nx.DiGraph(graph)
 
         self._num_classes = info['num_classes']
         self._g.ndata['train_mask'] = generate_mask_tensor(F.asnumpy(self._g.ndata['train_mask']))
@@ -250,10 +250,6 @@ class CitationGraphDataset(DGLBuiltinDataset):
     """ Citation graph is used in many examples
         We preserve these properties for compatability.
     """
-    @property
-    def graph(self):
-        deprecate_property('dataset.graph', 'dataset[0]')
-        return self._graph
 
     @property
     def train_mask(self):

tutorials/models/1_gnn/6_line_graph.py

@@ -96,7 +96,7 @@ from dgl.data import citation_graph as citegrh
 
 data = citegrh.load_cora()
 
-G = dgl.DGLGraph(data.graph)
+G = data[0]
 labels = th.tensor(data.labels)
 
 # find all the nodes labeled with class 0

tutorials/models/1_gnn/9_gat.py

@@ -306,7 +306,7 @@ def load_cora_data():
     features = torch.FloatTensor(data.features)
     labels = torch.LongTensor(data.labels)
     mask = torch.BoolTensor(data.train_mask)
-    g = DGLGraph(data.graph)
+    g = data[0]
     return g, features, labels, mask
 
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