NN Modules (PyTorch)¶
We welcome your contribution! If you want a model to be implemented in DGL as a NN module, please create an issue started with “[Feature Request] NN Module XXXModel”.
If you want to contribute a NN module, please create a pull request started with “[NN] XXXModel in PyTorch NN Modules” and our team member would review this PR.
Conv Layers¶
Torch modules for graph convolutions.
GraphConv¶

class
dgl.nn.pytorch.conv.
GraphConv
(in_feats, out_feats, norm=True, bias=True, activation=None)[source]¶ Bases:
torch.nn.modules.module.Module
Apply graph convolution over an input signal.
Graph convolution is introduced in GCN and can be described as below:
\[h_i^{(l+1)} = \sigma(b^{(l)} + \sum_{j\in\mathcal{N}(i)}\frac{1}{c_{ij}}h_j^{(l)}W^{(l)})\]where \(\mathcal{N}(i)\) is the neighbor set of node \(i\). \(c_{ij}\) is equal to the product of the square root of node degrees: \(\sqrt{\mathcal{N}(i)}\sqrt{\mathcal{N}(j)}\). \(\sigma\) is an activation function.
The model parameters are initialized as in the original implementation where the weight \(W^{(l)}\) is initialized using Glorot uniform initialization and the bias is initialized to be zero.
Notes
Zero in degree nodes could lead to invalid normalizer. A common practice to avoid this is to add a selfloop for each node in the graph, which can be achieved by:
>>> g = ... # some DGLGraph >>> g.add_edges(g.nodes(), g.nodes())
Parameters:  in_feats (int) – Input feature size.
 out_feats (int) – Output feature size.
 norm (bool, optional) – If True, the normalizer \(c_{ij}\) is applied. Default:
True
.  bias (bool, optional) – If True, adds a learnable bias to the output. Default:
True
.  activation (callable activation function/layer or None, optional) – If not None, applies an activation function to the updated node features.
Default:
None
.

weight
¶ torch.Tensor – The learnable weight tensor.

bias
¶ torch.Tensor – The learnable bias tensor.

forward
(graph, feat)[source]¶ Compute graph convolution.
Notes
 Input shape: \((N, *, \text{in_feats})\) where * means any number of additional dimensions, \(N\) is the number of nodes.
 Output shape: \((N, *, \text{out_feats})\) where all but the last dimension are the same shape as the input.
Parameters:  graph (DGLGraph) – The graph.
 feat (torch.Tensor) – The input feature
Returns: The output feature
Return type: torch.Tensor
RelGraphConv¶

class
dgl.nn.pytorch.conv.
RelGraphConv
(in_feat, out_feat, num_rels, regularizer='basis', num_bases=None, bias=True, activation=None, self_loop=False, dropout=0.0)[source]¶ Bases:
torch.nn.modules.module.Module
Relational graph convolution layer.
Relational graph convolution is introduced in “Modeling Relational Data with Graph Convolutional Networks” and can be described as below:
\[h_i^{(l+1)} = \sigma(\sum_{r\in\mathcal{R}} \sum_{j\in\mathcal{N}^r(i)}\frac{1}{c_{i,r}}W_r^{(l)}h_j^{(l)}+W_0^{(l)}h_i^{(l)})\]where \(\mathcal{N}^r(i)\) is the neighbor set of node \(i\) w.r.t. relation \(r\). \(c_{i,r}\) is the normalizer equal to \(\mathcal{N}^r(i)\). \(\sigma\) is an activation function. \(W_0\) is the selfloop weight.
The basis regularization decomposes \(W_r\) by:
\[W_r^{(l)} = \sum_{b=1}^B a_{rb}^{(l)}V_b^{(l)}\]where \(B\) is the number of bases.
The blockdiagonaldecomposition regularization decomposes \(W_r\) into \(B\) number of block diagonal matrices. We refer \(B\) as the number of bases.
Parameters:  in_feat (int) – Input feature size.
 out_feat (int) – Output feature size.
 num_rels (int) – Number of relations.
 regularizer (str) – Which weight regularizer to use “basis” or “bdd”
 num_bases (int, optional) – Number of bases. If is none, use number of relations. Default: None.
 bias (bool, optional) – True if bias is added. Default: True
 activation (callable, optional) – Activation function. Default: None
 self_loop (bool, optional) – True to include self loop message. Default: False
 dropout (float, optional) – Dropout rate. Default: 0.0

forward
(g, x, etypes, norm=None)[source]¶ Forward computation
Parameters:  g (DGLGraph) – The graph.
 x (torch.Tensor) –
 Input node features. Could be either
 \((V, D)\) dense tensor
 \((V,)\) int64 vector, representing the categorical values of each node. We then treat the input feature as an onehot encoding feature.
 etypes (torch.Tensor) – Edge type tensor. Shape: \((E,)\)
 norm (torch.Tensor) – Optional edge normalizer tensor. Shape: \((E, 1)\)
Returns: New node features.
Return type: torch.Tensor
TAGConv¶

class
dgl.nn.pytorch.conv.
TAGConv
(in_feats, out_feats, k=2, bias=True, activation=None)[source]¶ Bases:
torch.nn.modules.module.Module
Topology Adaptive Graph Convolutional layer from paper Topology Adaptive Graph Convolutional Networks.
\[\mathbf{X}^{\prime} = \sum_{k=0}^K \mathbf{D}^{1/2} \mathbf{A} \mathbf{D}^{1/2}\mathbf{X} \mathbf{\Theta}_{k},\]where \(\mathbf{A}\) denotes the adjacency matrix and \(D_{ii} = \sum_{j=0} A_{ij}\) its diagonal degree matrix.
Parameters:  in_feats (int) – Input feature size.
 out_feats (int) – Output feature size.
 k (int, optional) – Number of hops :math: k. (default: 2)
 bias (bool, optional) – If True, adds a learnable bias to the output. Default:
True
.  activation (callable activation function/layer or None, optional) – If not None, applies an activation function to the updated node features.
Default:
None
.

lin
¶ torch.Module – The learnable linear module.

forward
(graph, feat)[source]¶ Compute topology adaptive graph convolution.
Parameters:  graph (DGLGraph) – The graph.
 feat (torch.Tensor) – The input feature of shape \((N, D_{in})\) where \(D_{in}\) is size of input feature, \(N\) is the number of nodes.
Returns: The output feature of shape \((N, D_{out})\) where \(D_{out}\) is size of output feature.
Return type: torch.Tensor
GATConv¶

class
dgl.nn.pytorch.conv.
GATConv
(in_feats, out_feats, num_heads, feat_drop=0.0, attn_drop=0.0, negative_slope=0.2, residual=False, activation=None)[source]¶ Bases:
torch.nn.modules.module.Module
Apply Graph Attention Network over an input signal.
\[h_i^{(l+1)} = \sum_{j\in \mathcal{N}(i)} \alpha_{i,j} W^{(l)} h_j^{(l)}\]where \(\alpha_{ij}\) is the attention score bewteen node \(i\) and node \(j\):
\[ \begin{align}\begin{aligned}\alpha_{ij}^{l} & = \mathrm{softmax_i} (e_{ij}^{l})\\e_{ij}^{l} & = \mathrm{LeakyReLU}\left(\vec{a}^T [W h_{i} \ W h_{j}]\right)\end{aligned}\end{align} \]Parameters:  in_feats (int) – Input feature size.
 out_feats (int) – Output feature size.
 num_heads (int) – Number of heads in MultiHead Attention.
 feat_drop (float, optional) – Dropout rate on feature, defaults:
0
.  attn_drop (float, optional) – Dropout rate on attention weight, defaults:
0
.  negative_slope (float, optional) – LeakyReLU angle of negative slope.
 residual (bool, optional) – If True, use residual connection.
 activation (callable activation function/layer or None, optional.) – If not None, applies an activation function to the updated node features.
Default:
None
.

forward
(graph, feat)[source]¶ Compute graph attention network layer.
Parameters:  graph (DGLGraph) – The graph.
 feat (torch.Tensor) – The input feature of shape \((N, D_{in})\) where \(D_{in}\) is size of input feature, \(N\) is the number of nodes.
Returns: The output feature of shape \((N, H, D_{out})\) where \(H\) is the number of heads, and \(D_{out}\) is size of output feature.
Return type: torch.Tensor
EdgeConv¶

class
dgl.nn.pytorch.conv.
EdgeConv
(in_feat, out_feat, batch_norm=False)[source]¶ Bases:
torch.nn.modules.module.Module
EdgeConv layer.
Introduced in “Dynamic Graph CNN for Learning on Point Clouds”. Can be described as follows:
\[x_i^{(l+1)} = \max_{j \in \mathcal{N}(i)} \mathrm{ReLU}( \Theta \cdot (x_j^{(l)}  x_i^{(l)}) + \Phi \cdot x_i^{(l)})\]where \(\mathcal{N}(i)\) is the neighbor of \(i\).
Parameters:
SAGEConv¶

class
dgl.nn.pytorch.conv.
SAGEConv
(in_feats, out_feats, aggregator_type, feat_drop=0.0, bias=True, norm=None, activation=None)[source]¶ Bases:
torch.nn.modules.module.Module
GraphSAGE layer from paper Inductive Representation Learning on Large Graphs.
\[ \begin{align}\begin{aligned}h_{\mathcal{N}(i)}^{(l+1)} & = \mathrm{aggregate} \left(\{h_{j}^{l}, \forall j \in \mathcal{N}(i) \}\right)\\h_{i}^{(l+1)} & = \sigma \left(W \cdot \mathrm{concat} (h_{i}^{l}, h_{\mathcal{N}(i)}^{l+1} + b) \right)\\h_{i}^{(l+1)} & = \mathrm{norm}(h_{i}^{l})\end{aligned}\end{align} \]Parameters:  in_feats (int) – Input feature size.
 out_feats (int) – Output feature size.
 feat_drop (float) – Dropout rate on features, default:
0
.  aggregator_type (str) – Aggregator type to use (
mean
,gcn
,pool
,lstm
).  bias (bool) – If True, adds a learnable bias to the output. Default:
True
.  norm (callable activation function/layer or None, optional) – If not None, applies normalization to the updated node features.
 activation (callable activation function/layer or None, optional) – If not None, applies an activation function to the updated node features.
Default:
None
.

forward
(graph, feat)[source]¶ Compute GraphSAGE layer.
Parameters:  graph (DGLGraph) – The graph.
 feat (torch.Tensor) – The input feature of shape \((N, D_{in})\) where \(D_{in}\) is size of input feature, \(N\) is the number of nodes.
Returns: The output feature of shape \((N, D_{out})\) where \(D_{out}\) is size of output feature.
Return type: torch.Tensor
SGConv¶

class
dgl.nn.pytorch.conv.
SGConv
(in_feats, out_feats, k=1, cached=False, bias=True, norm=None)[source]¶ Bases:
torch.nn.modules.module.Module
Simplifying Graph Convolution layer from paper Simplifying Graph Convolutional Networks.
\[H^{l+1} = (\hat{D}^{1/2} \hat{A} \hat{D}^{1/2})^K H^{l} \Theta^{l}\]Parameters:  in_feats (int) – Number of input features.
 out_feats (int) – Number of output features.
 k (int) – Number of hops \(K\). Defaults:
1
.  cached (bool) –
If True, the module would cache
\[(\hat{D}^{\frac{1}{2}}\hat{A}\hat{D}^{\frac{1}{2}})^K X\Theta\]at the first forward call. This parameter should only be set to
True
in Transductive Learning setting.  bias (bool) – If True, adds a learnable bias to the output. Default:
True
.  norm (callable activation function/layer or None, optional) – If not None, applies normalization to the updated node features.

forward
(graph, feat)[source]¶ Compute Simplifying Graph Convolution layer.
Parameters:  graph (DGLGraph) – The graph.
 feat (torch.Tensor) – The input feature of shape \((N, D_{in})\) where \(D_{in}\) is size of input feature, \(N\) is the number of nodes.
Returns: The output feature of shape \((N, D_{out})\) where \(D_{out}\) is size of output feature.
Return type: torch.Tensor
Notes
If
cache
is se to True,feat
andgraph
should not change during training, or you will get wrong results.
APPNPConv¶

class
dgl.nn.pytorch.conv.
APPNPConv
(k, alpha, edge_drop=0.0)[source]¶ Bases:
torch.nn.modules.module.Module
Approximate Personalized Propagation of Neural Predictions layer from paper Predict then Propagate: Graph Neural Networks meet Personalized PageRank.
\[ \begin{align}\begin{aligned}H^{0} & = X\\H^{t+1} & = (1\alpha)\left(\hat{D}^{1/2} \hat{A} \hat{D}^{1/2} H^{t}\right) + \alpha H^{0}\end{aligned}\end{align} \]Parameters: 
forward
(graph, feat)[source]¶ Compute APPNP layer.
Parameters:  graph (DGLGraph) – The graph.
 feat (torch.Tensor) – The input feature of shape \((N, *)\) \(N\) is the number of nodes, and \(*\) could be of any shape.
Returns: The output feature of shape \((N, *)\) where \(*\) should be the same as input shape.
Return type: torch.Tensor

GINConv¶

class
dgl.nn.pytorch.conv.
GINConv
(apply_func, aggregator_type, init_eps=0, learn_eps=False)[source]¶ Bases:
torch.nn.modules.module.Module
Graph Isomorphism Network layer from paper How Powerful are Graph Neural Networks?.
\[h_i^{(l+1)} = f_\Theta \left((1 + \epsilon) h_i^{l} + \mathrm{aggregate}\left(\left\{h_j^{l}, j\in\mathcal{N}(i) \right\}\right)\right)\]Parameters:  apply_func (callable activation function/layer or None) – If not None, apply this function to the updated node feature, the \(f_\Theta\) in the formula.
 aggregator_type (str) – Aggregator type to use (
sum
,max
ormean
).  init_eps (float, optional) – Initial \(\epsilon\) value, default:
0
.  learn_eps (bool, optional) – If True, \(\epsilon\) will be a learnable parameter.

forward
(graph, feat)[source]¶ Compute Graph Isomorphism Network layer.
Parameters:  graph (DGLGraph) – The graph.
 feat (torch.Tensor) – The input feature of shape \((N, D)\) where \(D\)
could be any positive integer, \(N\) is the number
of nodes. If
apply_func
is not None, \(D\) should fit the input dimensionality requirement ofapply_func
.
Returns: The output feature of shape \((N, D_{out})\) where \(D_{out}\) is the output dimensionality of
apply_func
. Ifapply_func
is None, \(D_{out}\) should be the same as input dimensionality.Return type: torch.Tensor
GatedGraphConv¶

class
dgl.nn.pytorch.conv.
GatedGraphConv
(in_feats, out_feats, n_steps, n_etypes, bias=True)[source]¶ Bases:
torch.nn.modules.module.Module
Gated Graph Convolution layer from paper Gated Graph Sequence Neural Networks.
\[ \begin{align}\begin{aligned}h_{i}^{0} & = [ x_i \ \mathbf{0} ]\\a_{i}^{t} & = \sum_{j\in\mathcal{N}(i)} W_{e_{ij}} h_{j}^{t}\\h_{i}^{t+1} & = \mathrm{GRU}(a_{i}^{t}, h_{i}^{t})\end{aligned}\end{align} \]Parameters: 
forward
(graph, feat, etypes)[source]¶ Compute Gated Graph Convolution layer.
Parameters:  graph (DGLGraph) – The graph.
 feat (torch.Tensor) – The input feature of shape \((N, D_{in})\) where \(N\) is the number of nodes of the graph and \(D_{in}\) is the input feature size.
 etypes (torch.LongTensor) – The edge type tensor of shape \((E,)\) where \(E\) is the number of edges of the graph.
Returns: The output feature of shape \((N, D_{out})\) where \(D_{out}\) is the output feature size.
Return type: torch.Tensor

GMMConv¶

class
dgl.nn.pytorch.conv.
GMMConv
(in_feats, out_feats, dim, n_kernels, aggregator_type='sum', residual=False, bias=True)[source]¶ Bases:
torch.nn.modules.module.Module
The Gaussian Mixture Model Convolution layer from Geometric Deep Learning on Graphs and Manifolds using Mixture Model CNNs.
\[ \begin{align}\begin{aligned}h_i^{l+1} & = \mathrm{aggregate}\left(\left\{\frac{1}{K} \sum_{k}^{K} w_k(u_{ij}), \forall j\in \mathcal{N}(i)\right\}\right)\\w_k(u) & = \exp\left(\frac{1}{2}(u\mu_k)^T \Sigma_k^{1} (u  \mu_k)\right)\end{aligned}\end{align} \]Parameters:  in_feats (int) – Number of input features.
 out_feats (int) – Number of output features.
 dim (int) – Dimensionality of pseudocoordinte.
 n_kernels (int) – Number of kernels \(K\).
 aggregator_type (str) – Aggregator type (
sum
,mean
,max
).  residual (bool) – If True, use residual connection inside this layer. Default:
False
.  bias (bool) – If True, adds a learnable bias to the output. Default:
True
.

forward
(graph, feat, pseudo)[source]¶ Compute Gaussian Mixture Model Convolution layer.
Parameters:  graph (DGLGraph) – The graph.
 feat (torch.Tensor) – The input feature of shape \((N, D_{in})\) where \(N\) is the number of nodes of the graph and \(D_{in}\) is the input feature size.
 pseudo (torch.Tensor) – The pseudo coordinate tensor of shape \((E, D_{u})\) where \(E\) is the number of edges of the graph and \(D_{u}\) is the dimensionality of pseudo coordinate.
Returns: The output feature of shape \((N, D_{out})\) where \(D_{out}\) is the output feature size.
Return type: torch.Tensor
ChebConv¶

class
dgl.nn.pytorch.conv.
ChebConv
(in_feats, out_feats, k, bias=True)[source]¶ Bases:
torch.nn.modules.module.Module
Chebyshev Spectral Graph Convolution layer from paper Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering.
\[ \begin{align}\begin{aligned}h_i^{l+1} &= \sum_{k=0}^{K1} W^{k, l}z_i^{k, l}\\Z^{0, l} &= H^{l}\\Z^{1, l} &= \hat{L} \cdot H^{l}\\Z^{k, l} &= 2 \cdot \hat{L} \cdot Z^{k1, l}  Z^{k2, l}\\\hat{L} &= 2\left(I  \hat{D}^{1/2} \hat{A} \hat{D}^{1/2}\right)/\lambda_{max}  I\end{aligned}\end{align} \]Parameters: 
forward
(graph, feat, lambda_max=None)[source]¶ Compute ChebNet layer.
Parameters:  graph (DGLGraph or BatchedDGLGraph) – The graph.
 feat (torch.Tensor) – The input feature of shape \((N, D_{in})\) where \(D_{in}\) is size of input feature, \(N\) is the number of nodes.
 lambda_max (list or tensor or None, optional.) – A list(tensor) with length \(B\), stores the largest eigenvalue
of the normalized laplacian of each individual graph in
graph
, where \(B\) is the batch size of the input graph. Default: None. If None, this method would compute the list by callingdgl.laplacian_lambda_max
.
Returns: The output feature of shape \((N, D_{out})\) where \(D_{out}\) is size of output feature.
Return type: torch.Tensor

AGNNConv¶

class
dgl.nn.pytorch.conv.
AGNNConv
(init_beta=1.0, learn_beta=True)[source]¶ Bases:
torch.nn.modules.module.Module
Attentionbased Graph Neural Network layer from paper Attentionbased Graph Neural Network for SemiSupervised Learning.
\[H^{l+1} = P H^{l}\]where \(P\) is computed as:
\[P_{ij} = \mathrm{softmax}_i ( \beta \cdot \cos(h_i^l, h_j^l))\]Parameters: 
forward
(graph, feat)[source]¶ Compute AGNN layer.
Parameters:  graph (DGLGraph) – The graph.
 feat (torch.Tensor) – The input feature of shape \((N, *)\) \(N\) is the number of nodes, and \(*\) could be of any shape.
Returns: The output feature of shape \((N, *)\) where \(*\) should be the same as input shape.
Return type: torch.Tensor

NNConv¶

class
dgl.nn.pytorch.conv.
NNConv
(in_feats, out_feats, edge_func, aggregator_type, residual=False, bias=True)[source]¶ Bases:
torch.nn.modules.module.Module
Graph Convolution layer introduced in Neural Message Passing for Quantum Chemistry.
\[h_{i}^{l+1} = h_{i}^{l} + \mathrm{aggregate}\left(\left\{ f_\Theta (e_{ij}) \cdot h_j^{l}, j\in \mathcal{N}(i) \right\}\right)\]Parameters:  in_feats (int) – Input feature size.
 out_feats (int) – Output feature size.
 edge_func (callable activation function/layer) – Maps each edge feature to a vector of shape
(in_feats * out_feats)
as weight to compute messages. Also is the \(f_\Theta\) in the formula.  aggregator_type (str) – Aggregator type to use (
sum
,mean
ormax
).  residual (bool, optional) – If True, use residual connection. Default:
False
.  bias (bool, optional) – If True, adds a learnable bias to the output. Default:
True
.

forward
(graph, feat, efeat)[source]¶ Compute MPNN Graph Convolution layer.
Parameters:  graph (DGLGraph) – The graph.
 feat (torch.Tensor) – The input feature of shape \((N, D_{in})\) where \(N\) is the number of nodes of the graph and \(D_{in}\) is the input feature size.
 efeat (torch.Tensor) – The edge feature of shape \((N, *)\), should fit the input
shape requirement of
edge_nn
.
Returns: The output feature of shape \((N, D_{out})\) where \(D_{out}\) is the output feature size.
Return type: torch.Tensor
AtomicConv¶

class
dgl.nn.pytorch.conv.
AtomicConv
(interaction_cutoffs, rbf_kernel_means, rbf_kernel_scaling, features_to_use=None)[source]¶ Bases:
torch.nn.modules.module.Module
Atomic Convolution Layer from paper Atomic Convolutional Networks for Predicting ProteinLigand Binding Affinity.
We denote the type of atom \(i\) by \(z_i\) and the distance between atom \(i\) and \(j\) by \(r_{ij}\).
Distance Transformation
An atomic convolution layer first transforms distances with radial filters and then perform a pooling operation.
For radial filter indexed by \(k\), it projects edge distances with
\[h_{ij}^{k} = \exp(\gamma_{k}r_{ij}r_{k}^2)\]If \(r_{ij} < c_k\),
\[f_{ij}^{k} = 0.5 * \cos(\frac{\pi r_{ij}}{c_k} + 1),\]else,
\[f_{ij}^{k} = 0.\]Finally,
\[e_{ij}^{k} = h_{ij}^{k} * f_{ij}^{k}\]Aggregation
For each type \(t\), each atom collects distance information from all neighbor atoms of type \(t\):
\[p_{i, t}^{k} = \sum_{j\in N(i)} e_{ij}^{k} * 1(z_j == t)\]We concatenate the results for all RBF kernels and atom types.
Notes
 This convolution operation is designed for molecular graphs in Chemistry, but it might be possible to extend it to more general graphs.
 There seems to be an inconsistency about the definition of \(e_{ij}^{k}\) in the paper and the author’s implementation. We follow the author’s implementation. In the paper, \(e_{ij}^{k}\) was defined as \(\exp(\gamma_{k}r_{ij}r_{k}^2 * f_{ij}^{k})\).
 \(\gamma_{k}\), \(r_k\) and \(c_k\) are all learnable.
Parameters:  interaction_cutoffs (float32 tensor of shape (K)) – \(c_k\) in the equations above. Roughly they can be considered as learnable cutoffs and two atoms are considered as connected if the distance between them is smaller than the cutoffs. K for the number of radial filters.
 rbf_kernel_means (float32 tensor of shape (K)) – \(r_k\) in the equations above. K for the number of radial filters.
 rbf_kernel_scaling (float32 tensor of shape (K)) – \(\gamma_k\) in the equations above. K for the number of radial filters.
 features_to_use (None or float tensor of shape (T)) – In the original paper, these are atomic numbers to consider, representing the types of atoms. T for the number of types of atomic numbers. Default to None.

forward
(graph, feat, distances)[source]¶ Apply the atomic convolution layer.
Parameters:  graph (DGLGraph or BatchedDGLGraph) – Topology based on which message passing is performed.
 feat (Float32 tensor of shape (V, 1)) – Initial node features, which are atomic numbers in the paper. V for the number of nodes.
 distances (Float32 tensor of shape (E, 1)) – Distance between end nodes of edges. E for the number of edges.
Returns: Updated node representations. V for the number of nodes, K for the number of radial filters, and T for the number of types of atomic numbers.
Return type: Float32 tensor of shape (V, K * T)
Dense Conv Layers¶
DenseGraphConv¶

class
dgl.nn.pytorch.conv.
DenseGraphConv
(in_feats, out_feats, norm=True, bias=True, activation=None)[source]¶ Bases:
torch.nn.modules.module.Module
Graph Convolutional Network layer where the graph structure is given by an adjacency matrix. We recommend user to use this module when applying graph convolution on dense graphs.
Parameters:  in_feats (int) – Input feature size.
 out_feats (int) – Output feature size.
 norm (bool) – If True, the normalizer \(c_{ij}\) is applied. Default:
True
.  bias (bool) – If True, adds a learnable bias to the output. Default:
True
.  activation (callable activation function/layer or None, optional) – If not None, applies an activation function to the updated node features.
Default:
None
.
See also

forward
(adj, feat)[source]¶ Compute (Dense) Graph Convolution layer.
Parameters:  adj (torch.Tensor) – The adjacency matrix of the graph to apply Graph Convolution on, should be of shape \((N, N)\), where a row represents the destination and a column represents the source.
 feat (torch.Tensor) – The input feature of shape \((N, D_{in})\) where \(D_{in}\) is size of input feature, \(N\) is the number of nodes.
Returns: The output feature of shape \((N, D_{out})\) where \(D_{out}\) is size of output feature.
Return type: torch.Tensor
DenseSAGEConv¶

class
dgl.nn.pytorch.conv.
DenseSAGEConv
(in_feats, out_feats, feat_drop=0.0, bias=True, norm=None, activation=None)[source]¶ Bases:
torch.nn.modules.module.Module
GraphSAGE layer where the graph structure is given by an adjacency matrix. We recommend to use this module when appying GraphSAGE on dense graphs.
Note that we only support gcn aggregator in DenseSAGEConv.
Parameters:  in_feats (int) – Input feature size.
 out_feats (int) – Output feature size.
 feat_drop (float, optional) – Dropout rate on features. Default: 0.
 bias (bool) – If True, adds a learnable bias to the output. Default:
True
.  norm (callable activation function/layer or None, optional) – If not None, applies normalization to the updated node features.
 activation (callable activation function/layer or None, optional) – If not None, applies an activation function to the updated node features.
Default:
None
.
See also

forward
(adj, feat)[source]¶ Compute (Dense) Graph SAGE layer.
Parameters:  adj (torch.Tensor) – The adjacency matrix of the graph to apply Graph Convolution on, should be of shape \((N, N)\), where a row represents the destination and a column represents the source.
 feat (torch.Tensor) – The input feature of shape \((N, D_{in})\) where \(D_{in}\) is size of input feature, \(N\) is the number of nodes.
Returns: The output feature of shape \((N, D_{out})\) where \(D_{out}\) is size of output feature.
Return type: torch.Tensor
DenseChebConv¶

class
dgl.nn.pytorch.conv.
DenseChebConv
(in_feats, out_feats, k, bias=True)[source]¶ Bases:
torch.nn.modules.module.Module
Chebyshev Spectral Graph Convolution layer from paper Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering.
We recommend to use this module when applying ChebConv on dense graphs.
Parameters: See also

forward
(adj, feat, lambda_max=None)[source]¶ Compute (Dense) Chebyshev Spectral Graph Convolution layer.
Parameters:  adj (torch.Tensor) – The adjacency matrix of the graph to apply Graph Convolution on, should be of shape \((N, N)\), where a row represents the destination and a column represents the source.
 feat (torch.Tensor) – The input feature of shape \((N, D_{in})\) where \(D_{in}\) is size of input feature, \(N\) is the number of nodes.
 lambda_max (float or None, optional) – A float value indicates the largest eigenvalue of given graph. Default: None.
Returns: The output feature of shape \((N, D_{out})\) where \(D_{out}\) is size of output feature.
Return type: torch.Tensor

Global Pooling Layers¶
Torch modules for graph global pooling.
SumPooling¶

class
dgl.nn.pytorch.glob.
SumPooling
[source]¶ Bases:
torch.nn.modules.module.Module
Apply sum pooling over the nodes in the graph.
\[r^{(i)} = \sum_{k=1}^{N_i} x^{(i)}_k\]
forward
(graph, feat)[source]¶ Compute sum pooling.
Parameters:  graph (DGLGraph or BatchedDGLGraph) – The graph.
 feat (torch.Tensor) – The input feature with shape \((N, *)\) where \(N\) is the number of nodes in the graph.
Returns: The output feature with shape \((*)\) (if input graph is a BatchedDGLGraph, the result shape would be \((B, *)\).
Return type: torch.Tensor

AvgPooling¶

class
dgl.nn.pytorch.glob.
AvgPooling
[source]¶ Bases:
torch.nn.modules.module.Module
Apply average pooling over the nodes in the graph.
\[r^{(i)} = \frac{1}{N_i}\sum_{k=1}^{N_i} x^{(i)}_k\]
forward
(graph, feat)[source]¶ Compute average pooling.
Parameters:  graph (DGLGraph or BatchedDGLGraph) – The graph.
 feat (torch.Tensor) – The input feature with shape \((N, *)\) where \(N\) is the number of nodes in the graph.
Returns: The output feature with shape \((*)\) (if input graph is a BatchedDGLGraph, the result shape would be \((B, *)\).
Return type: torch.Tensor

MaxPooling¶

class
dgl.nn.pytorch.glob.
MaxPooling
[source]¶ Bases:
torch.nn.modules.module.Module
Apply max pooling over the nodes in the graph.
\[r^{(i)} = \max_{k=1}^{N_i}\left( x^{(i)}_k \right)\]
forward
(graph, feat)[source]¶ Compute max pooling.
Parameters:  graph (DGLGraph or BatchedDGLGraph) – The graph.
 feat (torch.Tensor) – The input feature with shape \((N, *)\) where \(N\) is the number of nodes in the graph.
Returns: The output feature with shape \((*)\) (if input graph is a BatchedDGLGraph, the result shape would be \((B, *)\).
Return type: torch.Tensor

SortPooling¶

class
dgl.nn.pytorch.glob.
SortPooling
(k)[source]¶ Bases:
torch.nn.modules.module.Module
Apply Sort Pooling (An EndtoEnd Deep Learning Architecture for Graph Classification) over the nodes in the graph.
Parameters: k (int) – The number of nodes to hold for each graph. 
forward
(graph, feat)[source]¶ Compute sort pooling.
Parameters:  graph (DGLGraph or BatchedDGLGraph) – The graph.
 feat (torch.Tensor) – The input feature with shape \((N, D)\) where \(N\) is the number of nodes in the graph.
Returns: The output feature with shape \((k * D)\) (if input graph is a BatchedDGLGraph, the result shape would be \((B, k * D)\).
Return type: torch.Tensor

GlobalAttentionPooling¶

class
dgl.nn.pytorch.glob.
GlobalAttentionPooling
(gate_nn, feat_nn=None)[source]¶ Bases:
torch.nn.modules.module.Module
Apply Global Attention Pooling (Gated Graph Sequence Neural Networks) over the nodes in the graph.
\[r^{(i)} = \sum_{k=1}^{N_i}\mathrm{softmax}\left(f_{gate} \left(x^{(i)}_k\right)\right) f_{feat}\left(x^{(i)}_k\right)\]Parameters:  gate_nn (torch.nn.Module) – A neural network that computes attention scores for each feature.
 feat_nn (torch.nn.Module, optional) – A neural network applied to each feature before combining them with attention scores.

forward
(graph, feat)[source]¶ Compute global attention pooling.
Parameters:  graph (DGLGraph) – The graph.
 feat (torch.Tensor) – The input feature with shape \((N, D)\) where \(N\) is the number of nodes in the graph.
Returns: The output feature with shape \((D)\) (if input graph is a BatchedDGLGraph, the result shape would be \((B, D)\).
Return type: torch.Tensor
Set2Set¶

class
dgl.nn.pytorch.glob.
Set2Set
(input_dim, n_iters, n_layers)[source]¶ Bases:
torch.nn.modules.module.Module
Apply Set2Set (Order Matters: Sequence to sequence for sets) over the nodes in the graph.
For each individual graph in the batch, set2set computes
\[ \begin{align}\begin{aligned}q_t &= \mathrm{LSTM} (q^*_{t1})\\\alpha_{i,t} &= \mathrm{softmax}(x_i \cdot q_t)\\r_t &= \sum_{i=1}^N \alpha_{i,t} x_i\\q^*_t &= q_t \Vert r_t\end{aligned}\end{align} \]for this graph.
Parameters: 
forward
(graph, feat)[source]¶ Compute set2set pooling.
Parameters:  graph (DGLGraph or BatchedDGLGraph) – The graph.
 feat (torch.Tensor) – The input feature with shape \((N, D)\) where \(N\) is the number of nodes in the graph.
Returns: The output feature with shape \((D)\) (if input graph is a BatchedDGLGraph, the result shape would be \((B, D)\).
Return type: torch.Tensor

SetTransformerEncoder¶

class
dgl.nn.pytorch.glob.
SetTransformerEncoder
(d_model, n_heads, d_head, d_ff, n_layers=1, block_type='sab', m=None, dropouth=0.0, dropouta=0.0)[source]¶ Bases:
torch.nn.modules.module.Module
The Encoder module in Set Transformer: A Framework for Attentionbased PermutationInvariant Neural Networks.
Parameters:  d_model (int) – Hidden size of the model.
 n_heads (int) – Number of heads.
 d_head (int) – Hidden size of each head.
 d_ff (int) – Kernel size in FFN (Positionwise FeedForward Network) layer.
 n_layers (int) – Number of layers.
 block_type (str) – Building block type: ‘sab’ (Set Attention Block) or ‘isab’ (Induced Set Attention Block).
 m (int or None) – Number of induced vectors in ISAB Block, set to None if block type is ‘sab’.
 dropouth (float) – Dropout rate of each sublayer.
 dropouta (float) – Dropout rate of attention heads.

forward
(graph, feat)[source]¶ Compute the Encoder part of Set Transformer.
Parameters:  graph (DGLGraph or BatchedDGLGraph) – The graph.
 feat (torch.Tensor) – The input feature with shape \((N, D)\) where \(N\) is the number of nodes in the graph.
Returns: The output feature with shape \((N, D)\).
Return type: torch.Tensor
SetTransformerDecoder¶

class
dgl.nn.pytorch.glob.
SetTransformerDecoder
(d_model, num_heads, d_head, d_ff, n_layers, k, dropouth=0.0, dropouta=0.0)[source]¶ Bases:
torch.nn.modules.module.Module
The Decoder module in Set Transformer: A Framework for Attentionbased PermutationInvariant Neural Networks.
Parameters:  d_model (int) – Hidden size of the model.
 num_heads (int) – Number of heads.
 d_head (int) – Hidden size of each head.
 d_ff (int) – Kernel size in FFN (Positionwise FeedForward Network) layer.
 n_layers (int) – Number of layers.
 k (int) – Number of seed vectors in PMA (Pooling by Multihead Attention) layer.
 dropouth (float) – Dropout rate of each sublayer.
 dropouta (float) – Dropout rate of attention heads.

forward
(graph, feat)[source]¶ Compute the decoder part of Set Transformer.
Parameters:  graph (DGLGraph or BatchedDGLGraph) – The graph.
 feat (torch.Tensor) – The input feature with shape \((N, D)\) where \(N\) is the number of nodes in the graph.
Returns: The output feature with shape \((D)\) (if input graph is a BatchedDGLGraph, the result shape would be \((B, D)\).
Return type: torch.Tensor
Utility Modules¶
Sequential¶

class
dgl.nn.pytorch.utils.
Sequential
(*args)[source]¶ Bases:
torch.nn.modules.container.Sequential
A squential container for stacking graph neural network modules.
We support two modes: sequentially apply GNN modules on the same graph or a list of given graphs. In the second case, the number of graphs equals the number of modules inside this container.
Parameters: *args – Submodules of type torch.nn.Module, will be added to the container in the order they are passed in the constructor. Examples
Mode 1: sequentially apply GNN modules on the same graph
>>> import torch >>> import dgl >>> import torch.nn as nn >>> import dgl.function as fn >>> from dgl.nn.pytorch import Sequential >>> class ExampleLayer(nn.Module): >>> def __init__(self): >>> super().__init__() >>> def forward(self, graph, n_feat, e_feat): >>> graph = graph.local_var() >>> graph.ndata['h'] = n_feat >>> graph.update_all(fn.copy_u('h', 'm'), fn.sum('m', 'h')) >>> n_feat += graph.ndata['h'] >>> graph.apply_edges(fn.u_add_v('h', 'h', 'e')) >>> e_feat += graph.edata['e'] >>> return n_feat, e_feat >>> >>> g = dgl.DGLGraph() >>> g.add_nodes(3) >>> g.add_edges([0, 1, 2, 0, 1, 2, 0, 1, 2], [0, 0, 0, 1, 1, 1, 2, 2, 2]) >>> net = Sequential(ExampleLayer(), ExampleLayer(), ExampleLayer()) >>> n_feat = torch.rand(3, 4) >>> e_feat = torch.rand(9, 4) >>> net(g, n_feat, e_feat) (tensor([[39.8597, 45.4542, 25.1877, 30.8086], [40.7095, 45.3985, 25.4590, 30.0134], [40.7894, 45.2556, 25.5221, 30.4220]]), tensor([[80.3772, 89.7752, 50.7762, 60.5520], [80.5671, 89.3736, 50.6558, 60.6418], [80.4620, 89.5142, 50.3643, 60.3126], [80.4817, 89.8549, 50.9430, 59.9108], [80.2284, 89.6954, 50.0448, 60.1139], [79.7846, 89.6882, 50.5097, 60.6213], [80.2654, 90.2330, 50.2787, 60.6937], [80.3468, 90.0341, 50.2062, 60.2659], [80.0556, 90.2789, 50.2882, 60.5845]]))
Mode 2: sequentially apply GNN modules on different graphs
>>> import torch >>> import dgl >>> import torch.nn as nn >>> import dgl.function as fn >>> import networkx as nx >>> from dgl.nn.pytorch import Sequential >>> class ExampleLayer(nn.Module): >>> def __init__(self): >>> super().__init__() >>> def forward(self, graph, n_feat): >>> graph = graph.local_var() >>> 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) >>> >>> g1 = dgl.DGLGraph(nx.erdos_renyi_graph(32, 0.05)) >>> g2 = dgl.DGLGraph(nx.erdos_renyi_graph(16, 0.2)) >>> g3 = dgl.DGLGraph(nx.erdos_renyi_graph(8, 0.8)) >>> net = Sequential(ExampleLayer(), ExampleLayer(), ExampleLayer()) >>> n_feat = torch.rand(32, 4) >>> net([g1, g2, g3], n_feat) tensor([[209.6221, 225.5312, 193.8920, 220.1002], [250.0169, 271.9156, 240.2467, 267.7766], [220.4007, 239.7365, 213.8648, 234.9637], [196.4630, 207.6319, 184.2927, 208.7465]])
KNNGraph¶

class
dgl.nn.pytorch.factory.
KNNGraph
(k)[source]¶ Bases:
torch.nn.modules.module.Module
Layer that transforms one point set into a graph, or a batch of point sets with the same number of points into a union of those graphs.
If a batch of point set is provided, then the point \(j\) in point set \(i\) is mapped to graph node ID \(i \times M + j\), where \(M\) is the number of nodes in each point set.
The predecessors of each node are the knearest neighbors of the corresponding point.
Parameters: k (int) – The number of neighbors
SegmentedKNNGraph¶

class
dgl.nn.pytorch.factory.
SegmentedKNNGraph
(k)[source]¶ Bases:
torch.nn.modules.module.Module
Layer that transforms one point set into a graph, or a batch of point sets with different number of points into a union of those graphs.
If a batch of point set is provided, then the point \(j\) in point set \(i\) is mapped to graph node ID \(\sum_{p<i} V_p + j\), where \(V_p\) means the number of points in point set \(p\).
The predecessors of each node are the knearest neighbors of the corresponding point.
Parameters: k (int) – The number of neighbors 
forward
(x, segs)[source]¶ Forward computation.
Parameters:  x (Tensor) – \((M, D)\) where \(M\) means the total number of points in all point sets.
 segs (iterable of int) – \((N)\) integers where \(N\) means the number of point sets. The elements must sum up to \(M\).
Returns: A DGLGraph with no features.
Return type:

Edge Softmax¶
Torch modules for graph related softmax.

dgl.nn.pytorch.softmax.
edge_softmax
(graph, logits, eids='__ALL__')[source]¶ Compute edge softmax.
For a node \(i\), edge softmax is an operation of computing
\[a_{ij} = \frac{\exp(z_{ij})}{\sum_{j\in\mathcal{N}(i)}\exp(z_{ij})}\]where \(z_{ij}\) is a signal of edge \(j\rightarrow i\), also called logits in the context of softmax. \(\mathcal{N}(i)\) is the set of nodes that have an edge to \(i\).
An example of using edge softmax is in Graph Attention Network where the attention weights are computed with such an edge softmax operation.
Parameters:  graph (DGLGraph) – The graph to perform edge softmax
 logits (torch.Tensor) – The input edge feature
 eids (torch.Tensor or ALL, optional) – Edges on which to apply edge softmax. If ALL, apply edge softmax on all edges in the graph. Default: ALL.
Returns: Softmax value
Return type: Tensor
Notes
 Input shape: \((E, *, 1)\) where * means any number of additional dimensions, \(E\) equals the length of eids. If eids is ALL, \(E\) equals number of edges in the graph.
 Return shape: \((E, *, 1)\)
Examples
>>> from dgl.nn.pytorch.softmax import edge_softmax >>> import dgl >>> import torch as th
Create a
DGLGraph
object and initialize its edge features.>>> g = dgl.DGLGraph() >>> g.add_nodes(3) >>> g.add_edges([0, 0, 0, 1, 1, 2], [0, 1, 2, 1, 2, 2]) >>> edata = th.ones(6, 1).float() >>> edata tensor([[1.], [1.], [1.], [1.], [1.], [1.]])
Apply edge softmax on g:
>>> edge_softmax(g, edata) tensor([[1.0000], [0.5000], [0.3333], [0.5000], [0.3333], [0.3333]])
Apply edge softmax on first 4 edges of g:
>>> edge_softmax(g, edata[:4], th.Tensor([0,1,2,3])) tensor([[1.0000], [0.5000], [1.0000], [0.5000]])