Hi, thanks for making this awesome graph library.

I’m trying to implement the GCN and reproduce the result in Semi-Supervised Classification with Graph Convolutional Networks.

With support of Graph Convolutional Network tutorial, I can make the simple GCN and obtained some good accuracy.

But the implementation of normalization of the adjacency matrix did not go well.

I tried

```
class GCN(nn.Module):
def __init__(self, in_feats, out_feats, activation):
super(GCN, self).__init__()
self.apply_mod = NodeApplyModule(in_feats, out_feats, activation)
def forward(self, g, feature):
g.ndata['h'] = feature
# --- normalization ---
g.ndata['h'] = g.ndata['h'] / g.ndata['sqrt_deg']
g.update_all(fn.copy_src(src='h', out='m'),
fn.sum(msg='m', out='m_sum'))
g.ndata['h'] = g.ndata['m_sum'] / g.ndata['sqrt_deg']
# ---------------------
g.apply_nodes(func=self.apply_mod)
return g.ndata.pop('h')
```

with

```
g = DGLGraph(data.graph)
g.ndata['deg'] = g.out_degrees(g.nodes()).float()
g.ndata['deg'] = g.ndata['deg'].view(2708, 1)
g.ndata['sqrt_deg'] = th.sqrt(g.ndata['deg'])
```

But this method had bad accuracy…

Please tell me some good idea.