[Misc] Rename number_of_edges and number_of_nodes to num_edges and num_nodes. (#5488)

* nn-only

* data-only

---------
Co-authored-by: Ubuntu <ubuntu@ip-172-31-28-63.ap-northeast-1.compute.internal>

python/dgl/nn/pytorch/hetero.py

@@ -62,7 +62,7 @@ class HeteroGraphConv(nn.Module):
     ``'user'`` and ``'game'`` nodes.
 
     >>> import torch as th
-    >>> h1 = {'user' : th.randn((g.number_of_nodes('user'), 5))}
+    >>> h1 = {'user' : th.randn((g.num_nodes('user'), 5))}
     >>> h2 = conv(g, h1)
     >>> print(h2.keys())
     dict_keys(['user', 'game'])

python/dgl/nn/pytorch/sparse_emb.py

@@ -57,7 +57,7 @@ class NodeEmbedding:  # NodeEmbedding
 
     In each training process
 
-    >>> emb = dgl.nn.NodeEmbedding(g.number_of_nodes(), 10, 'emb', init_func=initializer)
+    >>> emb = dgl.nn.NodeEmbedding(g.num_nodes(), 10, 'emb', init_func=initializer)
     >>> optimizer = dgl.optim.SparseAdam([emb], lr=0.001)
     >>> for blocks in dataloader:
     ...     ...

python/dgl/nn/pytorch/utils.py

@@ -182,7 +182,7 @@ class Sequential(nn.Sequential):
     >>>             graph.ndata['h'] = n_feat
     >>>             graph.update_all(fn.copy_u('h', 'm'), fn.sum('m', 'h'))
     >>>             n_feat += graph.ndata['h']
-    >>>             return n_feat.view(graph.number_of_nodes() // 2, 2, -1).sum(1)
+    >>>             return n_feat.view(graph.num_nodes() // 2, 2, -1).sum(1)
     >>>
     >>> g1 = dgl.DGLGraph(nx.erdos_renyi_graph(32, 0.05))
     >>> g2 = dgl.DGLGraph(nx.erdos_renyi_graph(16, 0.2))

python/dgl/nn/tensorflow/conv/appnpconv.py

@@ -66,9 +66,7 @@ class APPNPConv(layers.Layer):
                 # normalization by src node
                 feat = feat * norm
                 graph.ndata["h"] = feat
-                graph.edata["w"] = self.edge_drop(
-                    tf.ones(graph.number_of_edges(), 1)
-                )
+                graph.edata["w"] = self.edge_drop(tf.ones(graph.num_edges(), 1))
                 graph.update_all(fn.u_mul_e("h", "w", "m"), fn.sum("m", "h"))
                 feat = graph.ndata.pop("h")
                 # normalization by dst node

python/dgl/nn/tensorflow/conv/sageconv.py

@@ -165,7 +165,7 @@ class SAGEConv(layers.Layer):
             h_self = feat_dst
 
             # Handle the case of graphs without edges
-            if graph.number_of_edges() == 0:
+            if graph.num_edges() == 0:
                 graph.dstdata["neigh"] = tf.cast(
                     tf.zeros((graph.number_of_dst_nodes(), self._in_src_feats)),
                     tf.float32,

python/dgl/nn/tensorflow/hetero.py

@@ -58,7 +58,7 @@ class HeteroGraphConv(layers.Layer):
     ``'user'`` and ``'game'`` nodes.
 
     >>> import tensorflow as tf
-    >>> h1 = {'user' : tf.random.normal((g.number_of_nodes('user'), 5))}
+    >>> h1 = {'user' : tf.random.normal((g.num_nodes('user'), 5))}
     >>> h2 = conv(g, h1)
     >>> print(h2.keys())
     dict_keys(['user', 'game'])