[NN] Add commonly used GNN models from examples to dgl.nn modules. (#748)

* gat

* upd

* upd sage

* upd

* upd

* upd

* upd

* upd

* add gmmconv

* upd ggnn

* upd

* upd

* upd

* upd

* add citation examples

* add README

* fix cheb

* improve doc

* formula

* upd

* trigger

* lint

* lint

* upd

* add test for transform

* add test

* check

* upd

* improve doc

* shape check

* upd

* densechebconv, currently not correct (?)

* fix cheb

* fix

* upd

* upd sgc-reddit

* upd

* trigger

docs/source/api/python/function.rst

@@ -14,6 +14,32 @@ Message functions
     copy_src
     copy_edge
     src_mul_edge
+    copy_u
+    copy_e
+    u_add_v
+    u_sub_v
+    u_mul_v
+    u_div_v
+    u_add_e
+    u_sub_e
+    u_mul_e
+    u_div_e
+    v_add_u
+    v_sub_u
+    v_mul_u
+    v_div_u
+    v_add_e
+    v_sub_e
+    v_mul_e
+    v_div_e
+    e_add_u
+    e_sub_u
+    e_mul_u
+    e_div_u
+    e_add_v
+    e_sub_v
+    e_mul_v
+    e_div_v
 
 Reduce functions
 ----------------
@@ -23,3 +49,6 @@ Reduce functions
 
     sum
     max
+    min
+    prod
+    mean

docs/source/api/python/nn.pytorch.rst

@@ -16,6 +16,62 @@ dgl.nn.pytorch.conv
     :members: forward
     :show-inheritance:
 
+.. autoclass:: dgl.nn.pytorch.conv.TAGConv
+    :members: forward
+    :show-inheritance:
+
+.. autoclass:: dgl.nn.pytorch.conv.GATConv
+    :members: forward
+    :show-inheritance:
+
+.. autoclass:: dgl.nn.pytorch.conv.SAGEConv
+    :members: forward
+    :show-inheritance:
+
+.. autoclass:: dgl.nn.pytorch.conv.SGConv
+    :members: forward
+    :show-inheritance:
+
+.. autoclass:: dgl.nn.pytorch.conv.APPNPConv
+    :members: forward
+    :show-inheritance:
+
+.. autoclass:: dgl.nn.pytorch.conv.GINConv
+    :members: forward
+    :show-inheritance:
+
+.. autoclass:: dgl.nn.pytorch.conv.GatedGraphConv
+    :members: forward
+    :show-inheritance:
+
+.. autoclass:: dgl.nn.pytorch.conv.GMMConv
+    :members: forward
+    :show-inheritance:
+
+.. autoclass:: dgl.nn.pytorch.conv.ChebConv
+    :members: forward
+    :show-inheritance:
+
+.. autoclass:: dgl.nn.pytorch.conv.AGNNConv
+    :members: forward
+    :show-inheritance:
+
+.. autoclass:: dgl.nn.pytorch.conv.NNConv
+    :members: forward
+    :show-inheritance:
+
+.. autoclass:: dgl.nn.pytorch.conv.DenseGraphConv
+    :members: forward
+    :show-inheritance:
+
+.. autoclass:: dgl.nn.pytorch.conv.DenseSAGEConv
+    :members: forward
+    :show-inheritance:
+
+.. autoclass:: dgl.nn.pytorch.conv.DenseChebConv
+    :members: forward
+    :show-inheritance:
+
 dgl.nn.pytorch.glob
 -------------------
 .. automodule:: dgl.nn.pytorch.glob

docs/source/api/python/transform.rst

@@ -12,3 +12,6 @@ Transform -- Graph Transformation
     reverse
     to_simple_graph
     to_bidirected
+    khop_adj
+    khop_graph
+    laplacian_lambda_max

examples/pytorch/appnp/appnp.py

@@ -8,44 +8,7 @@ Author's code: https://github.com/klicperajo/ppnp
 import torch
 import torch.nn as nn
 import dgl.function as fn
-
-
-class GraphPropagation(nn.Module):
-    def __init__(self,
-                 g,
-                 edge_drop,
-                 alpha,
-                 k):
-        super(GraphPropagation, self).__init__()
-        self.g = g
-        self.alpha = alpha
-        self.k = k
-        if edge_drop:
-            self.edge_drop = nn.Dropout(edge_drop)
-        else:
-            self.edge_drop = 0.
-
-    def forward(self, h):
-        self.cached_h = h
-        for _ in range(self.k):
-            # normalization by square root of src degree
-            h = h * self.g.ndata['norm']
-            self.g.ndata['h'] = h
-            if self.edge_drop:
-                # performing edge dropout
-                ed = self.edge_drop(torch.ones((self.g.number_of_edges(), 1), device=h.device))
-                self.g.edata['d'] = ed
-                self.g.update_all(fn.src_mul_edge(src='h', edge='d', out='m'),
-                                  fn.sum(msg='m', out='h'))
-            else:
-                self.g.update_all(fn.copy_src(src='h', out='m'),
-                                  fn.sum(msg='m', out='h'))
-            h = self.g.ndata.pop('h')
-            # normalization by square root of dst degree
-            h = h * self.g.ndata['norm']
-            # update h using teleport probability alpha
-            h = h * (1 - self.alpha) + self.cached_h * self.alpha
-        return h
+from dgl.nn.pytorch.conv import APPNPConv
 
 
 class APPNP(nn.Module):
@@ -60,6 +23,7 @@ class APPNP(nn.Module):
                  alpha,
                  k):
         super(APPNP, self).__init__()
+        self.g = g
         self.layers = nn.ModuleList()
         # input layer
         self.layers.append(nn.Linear(in_feats, hiddens[0]))
@@ -73,7 +37,7 @@ class APPNP(nn.Module):
             self.feat_drop = nn.Dropout(feat_drop)
         else:
             self.feat_drop = lambda x: x
-        self.propagate = GraphPropagation(g, edge_drop, alpha, k)
+        self.propagate = APPNPConv(k, alpha, edge_drop)
         self.reset_parameters()
 
     def reset_parameters(self):
@@ -89,5 +53,5 @@ class APPNP(nn.Module):
             h = self.activation(layer(h))
         h = self.layers[-1](self.feat_drop(h))
         # propagation step
-        h = self.propagate(h)
+        h = self.propagate(h, self.g)
         return h

examples/pytorch/gat/gat.py

@@ -10,78 +10,8 @@ Pytorch implementation: https://github.com/Diego999/pyGAT
 import torch
 import torch.nn as nn
 import dgl.function as fn
-from dgl.nn.pytorch import edge_softmax
+from dgl.nn.pytorch import edge_softmax, GATConv
 
-class GraphAttention(nn.Module):
-    def __init__(self,
-                 g,
-                 in_dim,
-                 out_dim,
-                 num_heads,
-                 feat_drop,
-                 attn_drop,
-                 alpha,
-                 residual=False):
-        super(GraphAttention, self).__init__()
-        self.g = g
-        self.num_heads = num_heads
-        self.fc = nn.Linear(in_dim, num_heads * out_dim, bias=False)
-        if feat_drop:
-            self.feat_drop = nn.Dropout(feat_drop)
-        else:
-            self.feat_drop = lambda x : x
-        if attn_drop:
-            self.attn_drop = nn.Dropout(attn_drop)
-        else:
-            self.attn_drop = lambda x : x
-        self.attn_l = nn.Parameter(torch.Tensor(size=(1, num_heads, out_dim)))
-        self.attn_r = nn.Parameter(torch.Tensor(size=(1, num_heads, out_dim)))
-        nn.init.xavier_normal_(self.fc.weight.data, gain=1.414)
-        nn.init.xavier_normal_(self.attn_l.data, gain=1.414)
-        nn.init.xavier_normal_(self.attn_r.data, gain=1.414)
-        self.leaky_relu = nn.LeakyReLU(alpha)
-        self.softmax = edge_softmax
-        self.residual = residual
-        if residual:
-            if in_dim != out_dim:
-                self.res_fc = nn.Linear(in_dim, num_heads * out_dim, bias=False)
-                nn.init.xavier_normal_(self.res_fc.weight.data, gain=1.414)
-            else:
-                self.res_fc = None
-
-    def forward(self, inputs):
-        # prepare
-        h = self.feat_drop(inputs)  # NxD
-        ft = self.fc(h).reshape((h.shape[0], self.num_heads, -1))  # NxHxD'
-        a1 = (ft * self.attn_l).sum(dim=-1).unsqueeze(-1) # N x H x 1
-        a2 = (ft * self.attn_r).sum(dim=-1).unsqueeze(-1) # N x H x 1
-        self.g.ndata.update({'ft' : ft, 'a1' : a1, 'a2' : a2})
-        # 1. compute edge attention
-        self.g.apply_edges(self.edge_attention)
-        # 2. compute softmax
-        self.edge_softmax()
-        # 3. compute the aggregated node features scaled by the dropped,
-        # unnormalized attention values.
-        self.g.update_all(fn.src_mul_edge('ft', 'a_drop', 'ft'), fn.sum('ft', 'ft'))
-        ret = self.g.ndata['ft']
-        # 4. residual
-        if self.residual:
-            if self.res_fc is not None:
-                resval = self.res_fc(h).reshape((h.shape[0], self.num_heads, -1))  # NxHxD'
-            else:
-                resval = torch.unsqueeze(h, 1)  # Nx1xD'
-            ret = resval + ret
-        return ret
-
-    def edge_attention(self, edges):
-        # an edge UDF to compute unnormalized attention values from src and dst
-        a = self.leaky_relu(edges.src['a1'] + edges.dst['a2'])
-        return {'a' : a}
-
-    def edge_softmax(self):
-        attention = self.softmax(self.g, self.g.edata.pop('a'))
-        # Dropout attention scores and save them
-        self.g.edata['a_drop'] = self.attn_drop(attention)
 
 class GAT(nn.Module):
     def __init__(self,
@@ -94,7 +24,7 @@ class GAT(nn.Module):
                  activation,
                  feat_drop,
                  attn_drop,
-                 alpha,
+                 negative_slope,
                  residual):
         super(GAT, self).__init__()
         self.g = g
@@ -102,24 +32,24 @@ class GAT(nn.Module):
         self.gat_layers = nn.ModuleList()
         self.activation = activation
         # input projection (no residual)
-        self.gat_layers.append(GraphAttention(
-            g, in_dim, num_hidden, heads[0], feat_drop, attn_drop, alpha, False))
+        self.gat_layers.append(GATConv(
+            in_dim, num_hidden, heads[0],
+            feat_drop, attn_drop, negative_slope, False, self.activation))
         # hidden layers
         for l in range(1, num_layers):
             # due to multi-head, the in_dim = num_hidden * num_heads
-            self.gat_layers.append(GraphAttention(
-                g, num_hidden * heads[l-1], num_hidden, heads[l],
-                feat_drop, attn_drop, alpha, residual))
+            self.gat_layers.append(GATConv(
+                num_hidden * heads[l-1], num_hidden, heads[l],
+                feat_drop, attn_drop, negative_slope, residual, self.activation))
         # output projection
-        self.gat_layers.append(GraphAttention(
-            g, num_hidden * heads[-2], num_classes, heads[-1],
-            feat_drop, attn_drop, alpha, residual))
+        self.gat_layers.append(GATConv(
+            num_hidden * heads[-2], num_classes, heads[-1],
+            feat_drop, attn_drop, negative_slope, residual, None))
 
     def forward(self, inputs):
         h = inputs
         for l in range(self.num_layers):
-            h = self.gat_layers[l](h).flatten(1)
-            h = self.activation(h)
+            h = self.gat_layers[l](h, self.g).flatten(1)
         # output projection
-        logits = self.gat_layers[-1](h).mean(1)
+        logits = self.gat_layers[-1](h, self.g).mean(1)
         return logits

examples/pytorch/gat/train.py

@@ -86,7 +86,7 @@ def main(args):
                 F.elu,
                 args.in_drop,
                 args.attn_drop,
-                args.alpha,
+                args.negative_slope,
                 args.residual)
     print(model)
     stopper = EarlyStopping(patience=100)
@@ -161,8 +161,8 @@ if __name__ == '__main__':
                         help="learning rate")
     parser.add_argument('--weight-decay', type=float, default=5e-4,
                         help="weight decay")
-    parser.add_argument('--alpha', type=float, default=0.2,
-                        help="the negative slop of leaky relu")
+    parser.add_argument('--negative-slope', type=float, default=0.2,
+                        help="the negative slope of leaky relu")
     parser.add_argument('--fastmode', action="store_true", default=False,
                         help="skip re-evaluate the validation set")
     args = parser.parse_args()

examples/pytorch/gcn/train.py

@@ -57,8 +57,8 @@ def main(args):
     g = data.graph
     # add self loop
     if args.self_loop:
-            g.remove_edges_from(g.selfloop_edges())
-            g.add_edges_from(zip(g.nodes(), g.nodes()))
+        g.remove_edges_from(g.selfloop_edges())
+        g.add_edges_from(zip(g.nodes(), g.nodes()))
     g = DGLGraph(g)
     n_edges = g.number_of_edges()
     # normalization

examples/pytorch/gin/gin.py

@@ -9,72 +9,24 @@ Author's implementation: https://github.com/weihua916/powerful-gnns
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
+from dgl.nn.pytorch.conv import GINConv
 
 import dgl
 import dgl.function as fn
 
 
-# Sends a message of node feature h.
-msg = fn.copy_src(src='h', out='m')
-reduce_sum = fn.sum(msg='m', out='h')
-reduce_max = fn.max(msg='m', out='h')
-
-
-def reduce_mean(nodes):
-    return {'h': torch.mean(nodes.mailbox['m'], dim=1)[0]}
-
-
-class ApplyNodes(nn.Module):
+class ApplyNodeFunc(nn.Module):
     """Update the node feature hv with MLP, BN and ReLU."""
-    def __init__(self, mlp, layer):
-        super(ApplyNodes, self).__init__()
+    def __init__(self, mlp):
+        super(ApplyNodeFunc, self).__init__()
         self.mlp = mlp
         self.bn = nn.BatchNorm1d(self.mlp.output_dim)
-        self.layer = layer
 
-    def forward(self, nodes):
-        h = self.mlp(nodes.data['h'])
+    def forward(self, h):
+        h = self.mlp(h)
         h = self.bn(h)
         h = F.relu(h)
-
-        return {'h': h}
-
-
-class GINLayer(nn.Module):
-    """Neighbor pooling and reweight nodes before send graph into MLP"""
-    def __init__(self, eps, layer, mlp, neighbor_pooling_type, learn_eps):
-        super(GINLayer, self).__init__()
-        self.bn = nn.BatchNorm1d(mlp.output_dim)
-        self.neighbor_pooling_type = neighbor_pooling_type
-        self.eps = eps
-        self.learn_eps = learn_eps
-        self.layer = layer
-        self.apply_mod = ApplyNodes(mlp, layer)
-
-    def forward(self, g, feature):
-        g.ndata['h'] = feature
-
-        if self.neighbor_pooling_type == 'sum':
-            reduce_func = reduce_sum
-        elif self.neighbor_pooling_type == 'mean':
-            reduce_func = reduce_mean
-        elif self.neighbor_pooling_type == 'max':
-            reduce_func = reduce_max
-        else:
-            raise NotImplementedError()
-
-        h = feature  # h0
-        g.update_all(msg, reduce_func)
-        pooled = g.ndata['h']
-
-        # reweight the center node when aggregating it with its neighbors
-        if self.learn_eps:
-            pooled = pooled + (1 + self.eps[self.layer])*h
-
-        g.ndata['h'] = pooled
-        g.apply_nodes(func=self.apply_mod)
-
-        return g.ndata.pop('h')
+        return h
 
 
 class MLP(nn.Module):
@@ -168,7 +120,6 @@ class GIN(nn.Module):
         self.num_layers = num_layers
         self.graph_pooling_type = graph_pooling_type
         self.learn_eps = learn_eps
-        self.eps = nn.Parameter(torch.zeros(self.num_layers - 1))
 
         # List of MLPs
         self.ginlayers = torch.nn.ModuleList()
@@ -180,8 +131,8 @@ class GIN(nn.Module):
             else:
                 mlp = MLP(num_mlp_layers, hidden_dim, hidden_dim, hidden_dim)
 
-            self.ginlayers.append(GINLayer(
-                self.eps, layer, mlp, neighbor_pooling_type, self.learn_eps))
+            self.ginlayers.append(
+                GINConv(ApplyNodeFunc(mlp), neighbor_pooling_type, 0, self.learn_eps))
             self.batch_norms.append(nn.BatchNorm1d(hidden_dim))
 
         # Linear function for graph poolings of output of each layer
@@ -204,7 +155,7 @@ class GIN(nn.Module):
         hidden_rep = [h]
 
         for layer in range(self.num_layers - 1):
-            h = self.ginlayers[layer](g, h)
+            h = self.ginlayers[layer](h, g)
             hidden_rep.append(h)
 
         score_over_layer = 0

examples/pytorch/gin/main.py

@@ -148,7 +148,7 @@ def main(args):
 
         lrbar.set_description(
             "the learning eps with learn_eps={} is: {}".format(
-                args.learn_eps, model.eps.data))
+                args.learn_eps, [layer.eps.data for layer in model.ginlayers]))
 
     tbar.close()
     vbar.close()

examples/pytorch/graphsage/README.md

@@ -22,6 +22,6 @@ Run with following (available dataset: "cora", "citeseer", "pubmed")
 python3 graphsage.py --dataset cora --gpu 0
 ```
 
-* cora: ~0.8470 
-* citeseer: ~0.6870
-* pubmed: ~0.7730
+* cora: ~0.8330 
+* citeseer: ~0.7110
+* pubmed: ~0.7830

examples/pytorch/graphsage/graphsage.py

@@ -13,100 +13,7 @@ import torch.nn as nn
 import torch.nn.functional as F
 from dgl import DGLGraph
 from dgl.data import register_data_args, load_data
-import dgl.function as fn
-
-
-class Aggregator(nn.Module):
-    def __init__(self, g, in_feats, out_feats, activation=None, bias=True):
-        super(Aggregator, self).__init__()
-        self.g = g
-        self.linear = nn.Linear(in_feats, out_feats, bias=bias)  # (F, EF) or (2F, EF)
-        self.activation = activation
-        nn.init.xavier_uniform_(self.linear.weight, gain=nn.init.calculate_gain('relu'))
-
-    def forward(self, node):
-        nei = node.mailbox['m']  # (B, N, F)
-        h = node.data['h']  # (B, F)
-        h = self.concat(h, nei, node)  # (B, F) or (B, 2F)
-        h = self.linear(h)   # (B, EF)
-        if self.activation:
-            h = self.activation(h)
-        norm = torch.pow(h, 2)
-        norm = torch.sum(norm, 1, keepdim=True)
-        norm = torch.pow(norm, -0.5)
-        norm[torch.isinf(norm)] = 0
-        # h = h * norm
-        return {'h': h}
-
-    @abc.abstractmethod
-    def concat(self, h, nei, nodes):
-        raise NotImplementedError
-
-
-class MeanAggregator(Aggregator):
-    def __init__(self, g, in_feats, out_feats, activation, bias):
-        super(MeanAggregator, self).__init__(g, in_feats, out_feats, activation, bias)
-
-    def concat(self, h, nei, nodes):
-        degs = self.g.in_degrees(nodes.nodes()).float()
-        if h.is_cuda:
-            degs = degs.cuda(h.device)
-        concatenate = torch.cat((nei, h.unsqueeze(1)), 1)
-        concatenate = torch.sum(concatenate, 1) / degs.unsqueeze(1)
-        return concatenate  # (B, F)
-
-
-class PoolingAggregator(Aggregator):
-    def __init__(self, g, in_feats, out_feats, activation, bias):  # (2F, F)
-        super(PoolingAggregator, self).__init__(g, in_feats*2, out_feats, activation, bias)
-        self.mlp = PoolingAggregator.MLP(in_feats, in_feats, F.relu, False, True)
-
-    def concat(self, h, nei, nodes):
-        nei = self.mlp(nei)  # (B, F)
-        concatenate = torch.cat((nei, h), 1)  # (B, 2F)
-        return concatenate
-
-    class MLP(nn.Module):
-        def __init__(self, in_feats, out_feats, activation, dropout, bias):  # (F, F)
-            super(PoolingAggregator.MLP, self).__init__()
-            self.linear = nn.Linear(in_feats, out_feats, bias=bias)  # (F, F)
-            self.dropout = nn.Dropout(p=dropout)
-            self.activation = activation
-            nn.init.xavier_uniform_(self.linear.weight, gain=nn.init.calculate_gain('relu'))
-
-        def forward(self, nei):
-            nei = self.dropout(nei)  # (B, N, F)
-            nei = self.linear(nei)
-            if self.activation:
-                nei = self.activation(nei)
-            max_value = torch.max(nei, dim=1)[0]  # (B, F)
-            return max_value
-
-
-class GraphSAGELayer(nn.Module):
-    def __init__(self,
-                 g,
-                 in_feats,
-                 out_feats,
-                 activation,
-                 dropout,
-                 aggregator_type,
-                 bias=True,
-                 ):
-        super(GraphSAGELayer, self).__init__()
-        self.g = g
-        self.dropout = nn.Dropout(p=dropout)
-        if aggregator_type == "pooling":
-            self.aggregator = PoolingAggregator(g, in_feats, out_feats, activation, bias)
-        else:
-            self.aggregator = MeanAggregator(g, in_feats, out_feats, activation, bias)
-
-    def forward(self, h):
-        h = self.dropout(h)
-        self.g.ndata['h'] = h
-        self.g.update_all(fn.copy_src(src='h', out='m'), self.aggregator)
-        h = self.g.ndata.pop('h')
-        return h
+from dgl.nn.pytorch.conv import SAGEConv
 
 
 class GraphSAGE(nn.Module):
@@ -121,19 +28,20 @@ class GraphSAGE(nn.Module):
                  aggregator_type):
         super(GraphSAGE, self).__init__()
         self.layers = nn.ModuleList()
+        self.g = g
 
         # input layer
-        self.layers.append(GraphSAGELayer(g, in_feats, n_hidden, activation, dropout, aggregator_type))
+        self.layers.append(SAGEConv(in_feats, n_hidden, aggregator_type, feat_drop=dropout, activation=activation))
         # hidden layers
         for i in range(n_layers - 1):
-            self.layers.append(GraphSAGELayer(g, n_hidden, n_hidden, activation, dropout, aggregator_type))
+            self.layers.append(SAGEConv(n_hidden, n_hidden, aggregator_type, feat_drop=dropout, activation=activation))
         # output layer
-        self.layers.append(GraphSAGELayer(g, n_hidden, n_classes, None, dropout, aggregator_type))
+        self.layers.append(SAGEConv(n_hidden, n_classes, aggregator_type, feat_drop=dropout, activation=None)) # activation None
 
     def forward(self, features):
         h = features
         for layer in self.layers:
-            h = layer(h)
+            h = layer(h, self.g)
         return h
 
 
@@ -182,7 +90,9 @@ def main(args):
         print("use cuda:", args.gpu)
 
     # graph preprocess and calculate normalization factor
-    g = DGLGraph(data.graph)
+    g = data.graph
+    g.remove_edges_from(g.selfloop_edges())
+    g = DGLGraph(g)
     n_edges = g.number_of_edges()
 
     # create GraphSAGE model
@@ -231,7 +141,7 @@ def main(args):
 
 
 if __name__ == '__main__':
-    parser = argparse.ArgumentParser(description='GCN')
+    parser = argparse.ArgumentParser(description='GraphSAGE')
     register_data_args(parser)
     parser.add_argument("--dropout", type=float, default=0.5,
                         help="dropout probability")
@@ -247,8 +157,8 @@ if __name__ == '__main__':
                         help="number of hidden gcn layers")
     parser.add_argument("--weight-decay", type=float, default=5e-4,
                         help="Weight for L2 loss")
-    parser.add_argument("--aggregator-type", type=str, default="mean",
-                        help="Weight for L2 loss")
+    parser.add_argument("--aggregator-type", type=str, default="gcn",
+                        help="Aggregator type: mean/gcn/pool/lstm")
     args = parser.parse_args()
     print(args)
 

examples/pytorch/model_zoo/citation_network/README.md

+# Node Classification on Citation Networks
+
+This example shows how to use modules defined in `dgl.nn.pytorch.conv` to do node classification on
+citation network datasets.
+
+## Datasets
+
+- Cora
+- Citeseer
+- Pubmed
+
+## Models
+
+- GCN: [Semi-Supervised Classification with Graph Convolutional Networks](https://arxiv.org/pdf/1609.02907)
+- GAT: [Graph Attention Networks](https://arxiv.org/abs/1710.10903)
+- GraphSAGE [Inductive Representation Learning on Large Graphs](https://cs.stanford.edu/people/jure/pubs/graphsage-nips17.pdf)
+- APPNP: [Predict then Propagate: Graph Neural Networks meet Personalized PageRank](https://arxiv.org/pdf/1810.05997)
+- GIN: [How Powerful are Graph Neural Networks?](https://arxiv.org/abs/1810.00826)
+- TAGCN: [Topology Adaptive Graph Convolutional Networks](https://arxiv.org/abs/1710.10370)
+- SGC: [Simplifying Graph Convolutional Networks](https://arxiv.org/abs/1902.07153)
+- AGNN: [Attention-based Graph Neural Network for Semi-supervised Learning](https://arxiv.org/pdf/1803.03735.pdf)
+- ChebNet: [Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering](https://arxiv.org/abs/1606.09375)
+
+## Usage
+
+```
+python run.py [--gpu] --model MODEL_NAME --dataset DATASET_NAME [--self-loop]
+```
+
+The hyperparameters might not be the optimal, you could specify them manually in `conf.py`.

examples/pytorch/model_zoo/citation_network/conf.py

+import torch as th
+import torch.nn.functional as F
+
+GCN_CONFIG = {
+    'extra_args': [16, 1, F.relu, 0.5],
+    'lr': 1e-2,
+    'weight_decay': 5e-4,
+}
+
+GAT_CONFIG = {
+    'extra_args': [8, 1, [8] * 1 + [1], F.elu, 0.6, 0.6, 0.2, False],
+    'lr': 0.005,
+    'weight_decay': 5e-4,
+}
+
+GRAPHSAGE_CONFIG = {
+    'extra_args': [16, 1, F.relu, 0.5, 'gcn'],
+    'lr': 1e-2,
+    'weight_decay': 5e-4,
+}
+
+APPNP_CONFIG = {
+    'extra_args': [64, 1, F.relu, 0.5, 0.5, 0.1, 10],
+    'lr': 1e-2,
+    'weight_decay': 5e-4,
+}
+
+TAGCN_CONFIG = {
+    'extra_args': [16, 1, F.relu, 0.5],
+    'lr': 1e-2,
+    'weight_decay': 5e-4,
+}
+
+AGNN_CONFIG = {
+    'extra_args': [32, 2, 1.0, True, 0.5],
+    'lr': 1e-2,
+    'weight_decay': 5e-4,
+}
+
+SGC_CONFIG = {
+    'extra_args': [None, 2, False],
+    'lr': 0.2,
+    'weight_decay': 5e-6,
+}
+
+GIN_CONFIG = {
+    'extra_args': [16, 1, 0, True],
+    'lr': 1e-2,
+    'weight_decay': 5e-6,
+}
+
+CHEBNET_CONFIG = {
+    'extra_args': [16, 1, 3, True],
+    'lr': 1e-2,
+    'weight_decay': 5e-4,
+}

examples/pytorch/model_zoo/citation_network/models.py

+import torch
+import torch.nn as nn
+from dgl.nn.pytorch import GraphConv, GATConv, SAGEConv, GINConv,\
+    APPNPConv, TAGConv, SGConv, AGNNConv, ChebConv
+
+
+class GCN(nn.Module):
+    def __init__(self,
+                 g,
+                 in_feats,
+                 n_classes,
+                 n_hidden,
+                 n_layers,
+                 activation,
+                 dropout):
+        super(GCN, self).__init__()
+        self.g = g
+        self.layers = nn.ModuleList()
+        # input layer
+        self.layers.append(GraphConv(in_feats, n_hidden, activation=activation))
+        # hidden layers
+        for i in range(n_layers - 1):
+            self.layers.append(GraphConv(n_hidden, n_hidden, activation=activation))
+        # output layer
+        self.layers.append(GraphConv(n_hidden, n_classes))
+        self.dropout = nn.Dropout(p=dropout)
+
+    def forward(self, features):
+        h = features
+        for i, layer in enumerate(self.layers):
+            if i != 0:
+                h = self.dropout(h)
+            h = layer(h, self.g)
+        return h
+
+
+class GAT(nn.Module):
+    def __init__(self,
+                 g,
+                 in_dim,
+                 num_classes,
+                 num_hidden,
+                 num_layers,
+                 heads,
+                 activation,
+                 feat_drop,
+                 attn_drop,
+                 negative_slope,
+                 residual):
+        super(GAT, self).__init__()
+        self.g = g
+        self.num_layers = num_layers
+        self.gat_layers = nn.ModuleList()
+        self.activation = activation
+        # input projection (no residual)
+        self.gat_layers.append(GATConv(
+            in_dim, num_hidden, heads[0],
+            feat_drop, attn_drop, negative_slope, False, self.activation))
+        # hidden layers
+        for l in range(1, num_layers):
+            # due to multi-head, the in_dim = num_hidden * num_heads
+            self.gat_layers.append(GATConv(
+                num_hidden * heads[l-1], num_hidden, heads[l],
+                feat_drop, attn_drop, negative_slope, residual, self.activation))
+        # output projection
+        self.gat_layers.append(GATConv(
+            num_hidden * heads[-2], num_classes, heads[-1],
+            feat_drop, attn_drop, negative_slope, residual, None))
+
+    def forward(self, inputs):
+        h = inputs
+        for l in range(self.num_layers):
+            h = self.gat_layers[l](h, self.g).flatten(1)
+        # output projection
+        logits = self.gat_layers[-1](h, self.g).mean(1)
+        return logits
+
+
+class GraphSAGE(nn.Module):
+    def __init__(self,
+                 g,
+                 in_feats,
+                 n_classes,
+                 n_hidden,
+                 n_layers,
+                 activation,
+                 dropout,
+                 aggregator_type):
+        super(GraphSAGE, self).__init__()
+        self.layers = nn.ModuleList()
+        self.g = g
+
+        # input layer
+        self.layers.append(SAGEConv(in_feats, n_hidden, aggregator_type, feat_drop=dropout, activation=activation))
+        # hidden layers
+        for i in range(n_layers - 1):
+            self.layers.append(SAGEConv(n_hidden, n_hidden, aggregator_type, feat_drop=dropout, activation=activation))
+        # output layer
+        self.layers.append(SAGEConv(n_hidden, n_classes, aggregator_type, feat_drop=dropout, activation=None)) # activation None
+
+    def forward(self, features):
+        h = features
+        for layer in self.layers:
+            h = layer(h, self.g)
+        return h
+
+
+class APPNP(nn.Module):
+    def __init__(self,
+                 g,
+                 in_feats,
+                 n_classes,
+                 n_hidden,
+                 n_layers,
+                 activation,
+                 feat_drop,
+                 edge_drop,
+                 alpha,
+                 k):
+        super(APPNP, self).__init__()
+        self.g = g
+        self.layers = nn.ModuleList()
+        # input layer
+        self.layers.append(nn.Linear(in_feats, n_hidden))
+        # hidden layers
+        for i in range(1, n_layers):
+            self.layers.append(nn.Linear(n_hidden, n_hidden))
+        # output layer
+        self.layers.append(nn.Linear(n_hidden, n_classes))
+        self.activation = activation
+        if feat_drop:
+            self.feat_drop = nn.Dropout(feat_drop)
+        else:
+            self.feat_drop = lambda x: x
+        self.propagate = APPNPConv(k, alpha, edge_drop)
+        self.reset_parameters()
+
+    def reset_parameters(self):
+        for layer in self.layers:
+            layer.reset_parameters()
+
+    def forward(self, features):
+        # prediction step
+        h = features
+        h = self.feat_drop(h)
+        h = self.activation(self.layers[0](h))
+        for layer in self.layers[1:-1]:
+            h = self.activation(layer(h))
+        h = self.layers[-1](self.feat_drop(h))
+        # propagation step
+        h = self.propagate(h, self.g)
+        return h
+
+
+class TAGCN(nn.Module):
+    def __init__(self,
+                 g,
+                 in_feats,
+                 n_classes,
+                 n_hidden,
+                 n_layers,
+                 activation,
+                 dropout):
+        super(TAGCN, self).__init__()
+        self.g = g
+        self.layers = nn.ModuleList()
+        # input layer
+        self.layers.append(TAGConv(in_feats, n_hidden, activation=activation))
+        # hidden layers
+        for i in range(n_layers - 1):
+            self.layers.append(TAGConv(n_hidden, n_hidden, activation=activation))
+        # output layer
+        self.layers.append(TAGConv(n_hidden, n_classes)) #activation=None
+        self.dropout = nn.Dropout(p=dropout)
+
+    def forward(self, features):
+        h = features
+        for i, layer in enumerate(self.layers):
+            if i != 0:
+                h = self.dropout(h)
+            h = layer(h, self.g)
+        return h
+
+
+class AGNN(nn.Module):
+    def __init__(self,
+                 g,
+                 in_feats,
+                 n_classes,
+                 n_hidden,
+                 n_layers,
+                 init_beta,
+                 learn_beta,
+                 dropout):
+        super(AGNN, self).__init__()
+        self.g = g
+        self.layers = nn.ModuleList(
+            [AGNNConv(init_beta, learn_beta) for _ in range(n_layers)]
+        )
+        self.proj = nn.Sequential(
+            nn.Dropout(dropout),
+            nn.Linear(in_feats, n_hidden),
+            nn.ReLU()
+        )
+        self.cls = nn.Sequential(
+            nn.Dropout(dropout),
+            nn.Linear(n_hidden, n_classes)
+        )
+
+    def forward(self, features):
+        h = self.proj(features)
+        for layer in self.layers:
+            h = layer(h, self.g)
+        return self.cls(h)
+
+
+class SGC(nn.Module):
+    def __init__(self,
+                 g,
+                 in_feats,
+                 n_classes,
+                 n_hidden,
+                 k,
+                 bias):
+        super(SGC, self).__init__()
+        self.g = g
+        self.net = SGConv(in_feats,
+                          n_classes,
+                          k=k,
+                          cached=True,
+                          bias=bias)
+
+    def forward(self, features):
+        return self.net(features, self.g)
+
+
+class GIN(nn.Module):
+    def __init__(self,
+                 g,
+                 in_feats,
+                 n_classes,
+                 n_hidden,
+                 n_layers,
+                 init_eps,
+                 learn_eps):
+        super(GIN, self).__init__()
+        self.g = g
+        self.layers = nn.ModuleList()
+        self.layers.append(
+            GINConv(
+                nn.Sequential(
+                    nn.Dropout(0.6),
+                    nn.Linear(in_feats, n_hidden),
+                    nn.ReLU(),
+                ),
+                'mean',
+                init_eps,
+                learn_eps
+            )
+        )
+        for i in range(n_layers - 1):
+            self.layers.append(
+                GINConv(
+                    nn.Sequential(
+                        nn.Dropout(0.6),
+                        nn.Linear(n_hidden, n_hidden),
+                        nn.ReLU()
+                    ),
+                    'mean',
+                    init_eps,
+                    learn_eps
+                )
+            )
+        self.layers.append(
+            GINConv(
+                nn.Sequential(
+                    nn.Dropout(0.6),
+                    nn.Linear(n_hidden, n_classes),
+                ),
+                'mean',
+                init_eps,
+                learn_eps
+            )
+        )
+
+    def forward(self, features):
+        h = features
+        for layer in self.layers:
+            h = layer(h, self.g)
+        return h
+
+class ChebNet(nn.Module):
+    def __init__(self,
+                 g,
+                 in_feats,
+                 n_classes,
+                 n_hidden,
+                 n_layers,
+                 k,
+                 bias):
+        super(ChebNet, self).__init__()
+        self.g = g
+        self.layers = nn.ModuleList()
+        self.layers.append(
+            ChebConv(in_feats, n_hidden, k, bias)
+        )
+        for _ in range(n_layers - 1):
+            self.layers.append(
+                ChebConv(n_hidden, n_hidden, k, bias)
+            )
+
+        self.layers.append(
+            ChebConv(n_hidden, n_classes, k, bias)
+        )
+
+    def forward(self, features):
+        h = features
+        for layer in self.layers:
+            h = layer(h, self.g)
+        return h
\ No newline at end of file

examples/pytorch/model_zoo/citation_network/run.py

+import argparse, time
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from dgl import DGLGraph
+from dgl.data import register_data_args, load_data
+from models import *
+from conf import *
+
+
+def get_model_and_config(name):
+    name = name.lower()
+    if name == 'gcn':
+        return GCN, GCN_CONFIG
+    elif name == 'gat':
+        return GAT, GAT_CONFIG
+    elif name == 'graphsage':
+        return GraphSAGE, GRAPHSAGE_CONFIG
+    elif name == 'appnp':
+        return APPNP, APPNP_CONFIG
+    elif name == 'tagcn':
+        return TAGCN, TAGCN_CONFIG
+    elif name == 'agnn':
+        return AGNN, AGNN_CONFIG
+    elif name == 'sgc':
+        return SGC, SGC_CONFIG
+    elif name == 'gin':
+        return GIN, GIN_CONFIG
+    elif name == 'chebnet':
+        return ChebNet, CHEBNET_CONFIG
+
+def evaluate(model, features, labels, mask):
+    model.eval()
+    with torch.no_grad():
+        logits = model(features)
+        logits = logits[mask]
+        labels = labels[mask]
+        _, indices = torch.max(logits, dim=1)
+        correct = torch.sum(indices == labels)
+        return correct.item() * 1.0 / len(labels)
+
+def main(args):
+    # load and preprocess dataset
+    data = load_data(args)
+    features = torch.FloatTensor(data.features)
+    labels = torch.LongTensor(data.labels)
+    train_mask = torch.ByteTensor(data.train_mask)
+    val_mask = torch.ByteTensor(data.val_mask)
+    test_mask = torch.ByteTensor(data.test_mask)
+    in_feats = features.shape[1]
+    n_classes = data.num_labels
+    n_edges = data.graph.number_of_edges()
+    print("""----Data statistics------'
+      #Edges %d
+      #Classes %d
+      #Train samples %d
+      #Val samples %d
+      #Test samples %d""" %
+          (n_edges, n_classes,
+              train_mask.sum().item(),
+              val_mask.sum().item(),
+              test_mask.sum().item()))
+
+    if args.gpu < 0:
+        cuda = False
+    else:
+        cuda = True
+        torch.cuda.set_device(args.gpu)
+        features = features.cuda()
+        labels = labels.cuda()
+        train_mask = train_mask.cuda()
+        val_mask = val_mask.cuda()
+        test_mask = test_mask.cuda()
+
+    # graph preprocess and calculate normalization factor
+    g = data.graph
+    # add self loop
+    if args.self_loop:
+        g.remove_edges_from(g.selfloop_edges())
+        g.add_edges_from(zip(g.nodes(), g.nodes()))
+    g = DGLGraph(g)
+    n_edges = g.number_of_edges()
+    # normalization
+    degs = g.in_degrees().float()
+    norm = torch.pow(degs, -0.5)
+    norm[torch.isinf(norm)] = 0
+    if cuda:
+        norm = norm.cuda()
+    g.ndata['norm'] = norm.unsqueeze(1)
+
+    # create GCN model
+    GNN, config = get_model_and_config(args.model)
+    model = GNN(g,
+                in_feats,
+                n_classes,
+                *config['extra_args'])
+
+    if cuda:
+        model.cuda()
+
+    print(model)
+
+    loss_fcn = torch.nn.CrossEntropyLoss()
+
+    # use optimizer
+    optimizer = torch.optim.Adam(model.parameters(),
+                                 lr=config['lr'],
+                                 weight_decay=config['weight_decay'])
+
+    # initialize graph
+    dur = []
+    for epoch in range(200):
+        model.train()
+        if epoch >= 3:
+            t0 = time.time()
+        # forward
+        logits = model(features)
+        loss = loss_fcn(logits[train_mask], labels[train_mask])
+
+        optimizer.zero_grad()
+        loss.backward()
+        optimizer.step()
+
+        if epoch >= 3:
+            dur.append(time.time() - t0)
+
+        acc = evaluate(model, features, labels, val_mask)
+        print("Epoch {:05d} | Time(s) {:.4f} | Loss {:.4f} | Accuracy {:.4f} | "
+              "ETputs(KTEPS) {:.2f}". format(epoch, np.mean(dur), loss.item(),
+                                             acc, n_edges / np.mean(dur) / 1000))
+
+    print()
+    acc = evaluate(model, features, labels, test_mask)
+    print("Test Accuracy {:.4f}".format(acc))
+
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser(description='Node classification on citation networks.')
+    register_data_args(parser)
+    parser.add_argument("--model", type=str, default='gcn',
+                        help='model to use, available models are gcn, gat, graphsage, gin,'
+                             'appnp, tagcn, sgc, agnn')
+    parser.add_argument("--gpu", type=int, default=-1,
+            help="gpu")
+    parser.add_argument("--self-loop", action='store_true',
+            help="graph self-loop (default=False)")
+    args = parser.parse_args()
+    print(args)
+    main(args)
\ No newline at end of file

examples/pytorch/sgc/sgc.py

@@ -13,40 +13,13 @@ import torch.nn.functional as F
 import dgl.function as fn
 from dgl import DGLGraph
 from dgl.data import register_data_args, load_data
+from dgl.nn.pytorch.conv import SGConv
 
-class SGCLayer(nn.Module):
-    def __init__(self,
-                 g,
-                 h,
-                 in_feats,
-                 out_feats,
-                 bias=False,
-                 K=2):
-        super(SGCLayer, self).__init__()
-        self.g = g
-        self.weight = nn.Linear(in_feats, out_feats, bias=bias)
-        self.K = K
-        # precomputing message passing
-        for _ in range(self.K):
-            # normalization by square root of src degree
-            h = h * self.g.ndata['norm']
-            self.g.ndata['h'] = h
-            self.g.update_all(fn.copy_src(src='h', out='m'),
-                            fn.sum(msg='m', out='h'))
-            h = self.g.ndata.pop('h')
-            # normalization by square root of dst degree
-            h = h * self.g.ndata['norm']
-        # store precomputed result into a cached variable
-        self.cached_h = h
-
-    def forward(self, mask):
-        h = self.weight(self.cached_h[mask])
-        return h
-
-def evaluate(model, features, labels, mask):
+
+def evaluate(model, g, features, labels, mask):
     model.eval()
     with torch.no_grad():
-        logits = model(mask) # only compute the evaluation set
+        logits = model(features, g)[mask] # only compute the evaluation set
         labels = labels[mask]
         _, indices = torch.max(logits, dim=1)
         correct = torch.sum(indices == labels)
@@ -90,21 +63,13 @@ def main(args):
     n_edges = g.number_of_edges()
     # add self loop
     g.add_edges(g.nodes(), g.nodes())
-    # normalization
-    degs = g.in_degrees().float()
-    norm = torch.pow(degs, -0.5)
-    norm[torch.isinf(norm)] = 0
-    if cuda:
-        norm = norm.cuda()
-    g.ndata['norm'] = norm.unsqueeze(1)
 
     # create SGC model
-    model = SGCLayer(g,
-                     features,
-                     in_feats,
-                     n_classes,
-                     args.bias,
-                     K=2)
+    model = SGConv(in_feats,
+                   n_classes,
+                   k=2,
+                   cached=True,
+                   bias=args.bias)
 
     if cuda: model.cuda()
     loss_fcn = torch.nn.CrossEntropyLoss()
@@ -121,8 +86,8 @@ def main(args):
         if epoch >= 3:
             t0 = time.time()
         # forward
-        logits = model(train_mask) # only compute the train set
-        loss = loss_fcn(logits, labels[train_mask])
+        logits = model(features, g) # only compute the train set
+        loss = loss_fcn(logits[train_mask], labels[train_mask])
 
         optimizer.zero_grad()
         loss.backward()
@@ -131,13 +96,13 @@ def main(args):
         if epoch >= 3:
             dur.append(time.time() - t0)
 
-        acc = evaluate(model, features, labels, val_mask)
+        acc = evaluate(model, g, features, labels, val_mask)
         print("Epoch {:05d} | Time(s) {:.4f} | Loss {:.4f} | Accuracy {:.4f} | "
               "ETputs(KTEPS) {:.2f}". format(epoch, np.mean(dur), loss.item(),
                                              acc, n_edges / np.mean(dur) / 1000))
 
     print()
-    acc = evaluate(model, features, labels, test_mask)
+    acc = evaluate(model, g, features, labels, test_mask)
     print("Test Accuracy {:.4f}".format(acc))
 
 

examples/pytorch/sgc/sgc_reddit.py

@@ -13,38 +13,15 @@ import torch.nn.functional as F
 import dgl.function as fn
 from dgl import DGLGraph
 from dgl.data import register_data_args, load_data
+from dgl.nn.pytorch.conv import SGConv
 
-class SGCLayer(nn.Module):
-    def __init__(self,g,h,in_feats,out_feats,K=2):
-        super(SGCLayer, self).__init__()
-        self.g = g
-        self.weight = nn.Linear(in_feats, out_feats, bias=True)
-        self.K = K
-        # precomputing message passing
-        start = time.perf_counter()
-        for _ in range(self.K):
-            # normalization by square root of src degree
-            h = h * self.g.ndata['norm']
-            self.g.ndata['h'] = h
-            self.g.update_all(fn.copy_src(src='h', out='m'),
-                            fn.sum(msg='m', out='h'))
-            h = self.g.ndata.pop('h')
-            # normalization by square root of dst degree
-            h = h * self.g.ndata['norm']
-        h = (h-h.mean(0))/h.std(0)
-        precompute_elapse = time.perf_counter()-start
-        print("Precompute Time(s): {:.4f}".format(precompute_elapse))
-        # store precomputed result into a cached variable
-        self.cached_h = h
+def normalize(h):
+    return (h-h.mean(0))/h.std(0)
 
-    def forward(self, mask):
-        h = self.weight(self.cached_h[mask])
-        return h
-
-def evaluate(model, features, labels, mask):
+def evaluate(model, features, graph, labels, mask):
     model.eval()
     with torch.no_grad():
-        logits = model(mask) # only compute the evaluation set
+        logits = model(features, graph)[mask] # only compute the evaluation set
         labels = labels[mask]
         _, indices = torch.max(logits, dim=1)
         correct = torch.sum(indices == labels)
@@ -85,7 +62,6 @@ def main(args):
         test_mask = test_mask.cuda()
 
     # graph preprocess and calculate normalization factor
-    start = time.perf_counter()
     g = DGLGraph(data.graph)
     n_edges = g.number_of_edges()
     # normalization
@@ -94,14 +70,11 @@ def main(args):
     norm[torch.isinf(norm)] = 0
     if cuda: norm = norm.cuda()
     g.ndata['norm'] = norm.unsqueeze(1)
-    preprocess_elapse = time.perf_counter()-start
-    print("Preprocessing Time: {:.4f}".format(preprocess_elapse))
 
     # create SGC model
-    model = SGCLayer(g,features,in_feats,n_classes,K=2)
-
-    if cuda: model.cuda()
-    loss_fcn = torch.nn.CrossEntropyLoss()
+    model = SGConv(in_feats, n_classes, k=2, cached=True, bias=True, norm=normalize)
+    if args.gpu >= 0:
+        model = model.cuda()
 
     # use optimizer
     optimizer = torch.optim.LBFGS(model.parameters())
@@ -109,22 +82,17 @@ def main(args):
     # define loss closure
     def closure():
         optimizer.zero_grad()
-        output = model(train_mask)
+        output = model(features, g)[train_mask]
         loss_train = F.cross_entropy(output, labels[train_mask])
         loss_train.backward()
         return loss_train
 
     # initialize graph
-    dur = []
-    start = time.perf_counter()
     for epoch in range(args.n_epochs):
         model.train()
-        logits = model(train_mask) # only compute the train set
-        loss = optimizer.step(closure)
+        optimizer.step(closure)
 
-    train_elapse = time.perf_counter()-start
-    print("Train epoch {} | Train Time(s) {:.4f}".format(epoch, train_elapse))
-    acc = evaluate(model, features, labels, test_mask)
+    acc = evaluate(model, features, g, labels, test_mask)
     print("Test Accuracy {:.4f}".format(acc))
 
 

examples/pytorch/tagcn/tagcn.py

@@ -7,7 +7,7 @@ References:
 """
 import torch
 import torch.nn as nn
-from dgl.nn.pytorch.conv import TGConv
+from dgl.nn.pytorch.conv import TAGConv
 
 class TAGCN(nn.Module):
     def __init__(self,
@@ -22,12 +22,12 @@ class TAGCN(nn.Module):
         self.g = g
         self.layers = nn.ModuleList()
         # input layer
-        self.layers.append(TGConv(in_feats, n_hidden, activation=activation))
+        self.layers.append(TAGConv(in_feats, n_hidden, activation=activation))
         # hidden layers
         for i in range(n_layers - 1):
-            self.layers.append(TGConv(n_hidden, n_hidden, activation=activation))
+            self.layers.append(TAGConv(n_hidden, n_hidden, activation=activation))
         # output layer
-        self.layers.append(TGConv(n_hidden, n_classes)) #activation=None
+        self.layers.append(TAGConv(n_hidden, n_classes)) #activation=None
         self.dropout = nn.Dropout(p=dropout)
 
     def forward(self, features):

python/dgl/graph_index.py

@@ -479,7 +479,7 @@ class GraphIndex(ObjectBase):
 
         Returns
         -------
-        int
+        tensor
             The in degree array.
         """
         v_array = v.todgltensor()
@@ -510,7 +510,7 @@ class GraphIndex(ObjectBase):
 
         Returns
         -------
-        int
+        tensor
             The out degree array.
         """
         v_array = v.todgltensor()

python/dgl/nn/mxnet/conv.py

 """MXNet modules for graph convolutions."""
-# pylint: disable= no-member, arguments-differ
+# pylint: disable= no-member, arguments-differ, invalid-name
 import math
 import mxnet as mx
 from mxnet import gluon, nd