NN Modules (PyTorch)¶

Conv Layers¶

Torch modules for graph convolutions.

GraphConv¶

class dgl.nn.pytorch.conv.GraphConv(in_feats, out_feats, norm='both', weight=True, bias=True, activation=None, allow_zero_in_degree=False)[source]¶

Bases: torch.nn.modules.module.Module

Graph convolution was introduced in GCN and mathematically is defined as follows:

\[h_i^{(l+1)} = \sigma(b^{(l)} + \sum_{j\in\mathcal{N}(i)}\frac{1}{c_{ij}}h_j^{(l)}W^{(l)})\]

where \(\mathcal{N}(i)\) is the set of neighbors of node \(i\), \(c_{ij}\) is the product of the square root of node degrees (i.e., \(c_{ij} = \sqrt{|\mathcal{N}(i)|}\sqrt{|\mathcal{N}(j)|}\)), and \(\sigma\) is an activation function.

Parameters

in_feats (int) – Input feature size; i.e, the number of dimensions of \(h_j^{(l)}\).
out_feats (int) – Output feature size; i.e., the number of dimensions of \(h_i^{(l+1)}\).
norm (str, optional) – How to apply the normalizer. If is ‘right’, divide the aggregated messages by each node’s in-degrees, which is equivalent to averaging the received messages. If is ‘none’, no normalization is applied. Default is ‘both’, where the \(c_{ij}\) in the paper is applied.
weight (bool, optional) – If True, apply a linear layer. Otherwise, aggregating the messages without a weight matrix.
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.
allow_zero_in_degree (bool, optional) – If there are 0-in-degree nodes in the graph, output for those nodes will be invalid since no message will be passed to those nodes. This is harmful for some applications causing silent performance regression. This module will raise a DGLError if it detects 0-in-degree nodes in input graph. By setting True, it will suppress the check and let the users handle it by themselves. Default: False.

weight¶

The learnable weight tensor.

Type: torch.Tensor

bias¶

The learnable bias tensor.

Type: torch.Tensor

Note

Zero in-degree nodes will lead to invalid output value. This is because no message will be passed to those nodes, the aggregation function will be appied on empty input. A common practice to avoid this is to add a self-loop for each node in the graph if it is homogeneous, which can be achieved by:

>>> g = ... # a DGLGraph
>>> g = dgl.add_self_loop(g)

Calling add_self_loop will not work for some graphs, for example, heterogeneous graph since the edge type can not be decided for self_loop edges. Set allow_zero_in_degree to True for those cases to unblock the code and handle zere-in-degree nodes manually. A common practise to handle this is to filter out the nodes with zere-in-degree when use after conv.

Examples

>>> import dgl
>>> import numpy as np
>>> import torch as th
>>> from dgl.nn import GraphConv

>>> # Case 1: Homogeneous graph
>>> g = dgl.graph(([0,1,2,3,2,5], [1,2,3,4,0,3]))
>>> g = dgl.add_self_loop(g)
>>> feat = th.ones(6, 10)
>>> conv = GraphConv(10, 2, norm='both', weight=True, bias=True)
>>> res = conv(g, feat)
>>> print(res)
tensor([[ 1.3326, -0.2797],
        [ 1.4673, -0.3080],
        [ 1.3326, -0.2797],
        [ 1.6871, -0.3541],
        [ 1.7711, -0.3717],
        [ 1.0375, -0.2178]], grad_fn=<AddBackward0>)
>>> # allow_zero_in_degree example
>>> g = dgl.graph(([0,1,2,3,2,5], [1,2,3,4,0,3]))
>>> conv = GraphConv(10, 2, norm='both', weight=True, bias=True, allow_zero_in_degree=True)
>>> res = conv(g, feat)
>>> print(res)
tensor([[-0.2473, -0.4631],
        [-0.3497, -0.6549],
        [-0.3497, -0.6549],
        [-0.4221, -0.7905],
        [-0.3497, -0.6549],
        [ 0.0000,  0.0000]], grad_fn=<AddBackward0>)

>>> # Case 2: Unidirectional bipartite graph
>>> u = [0, 1, 0, 0, 1]
>>> v = [0, 1, 2, 3, 2]
>>> g = dgl.bipartite((u, v))
>>> u_fea = th.rand(2, 5)
>>> v_fea = th.rand(4, 5)
>>> conv = GraphConv(5, 2, norm='both', weight=True, bias=True)
>>> res = conv(g, (u_fea, v_fea))
>>> res
tensor([[-0.2994,  0.6106],
        [-0.4482,  0.5540],
        [-0.5287,  0.8235],
        [-0.2994,  0.6106]], grad_fn=<AddBackward0>)

forward(graph, feat, weight=None)[source]¶

Compute graph convolution.

Parameters

graph (DGLGraph) – The graph.
feat (torch.Tensor or pair of torch.Tensor) – If a torch.Tensor is given, it represents the input feature of shape \((N, D_{in})\) where \(D_{in}\) is size of input feature, \(N\) is the number of nodes. If a pair of torch.Tensor is given, which is the case for bipartite graph, the pair must contain two tensors of shape \((N_{in}, D_{in_{src}})\) and \((N_{out}, D_{in_{dst}})\).
weight (torch.Tensor, optional) – Optional external weight tensor.

Returns

The output feature

Return type

torch.Tensor

Raises

DGLError – Case 1: If there are 0-in-degree nodes in the input graph, it will raise DGLError since no message will be passed to those nodes. This will cause invalid output. The error can be ignored by setting allow_zero_in_degree parameter to True. Case 2: External weight is provided while at the same time the module has defined its own weight parameter.

Note

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.
Weight shape: \((\text{in_feats}, \text{out_feats})\).

reset_parameters()[source]¶: Reinitialize learnable parameters.

Note

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.

RelGraphConv¶

class dgl.nn.pytorch.conv.RelGraphConv(in_feat, out_feat, num_rels, regularizer='basis', num_bases=None, bias=True, activation=None, self_loop=True, low_mem=False, dropout=0.0, layer_norm=False)[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 self-loop 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, \(V_b^{(l)}\) are linearly combined with coefficients \(a_{rb}^{(l)}\).

The block-diagonal-decomposition regularization decomposes \(W_r\) into \(B\) number of block diagonal matrices. We refer \(B\) as the number of bases.

The block regularization decomposes \(W_r\) by:

\[W_r^{(l)} = \oplus_{b=1}^B Q_{rb}^{(l)}\]

where \(B\) is the number of bases, \(Q_{rb}^{(l)}\) are block bases with shape \(R^{(d^{(l+1)}/B)*(d^{l}/B)}\).

Parameters

in_feat (int) – Input feature size; i.e, the number of dimensions of \(h_j^{(l)}\).
out_feat (int) – Output feature size; i.e., the number of dimensions of \(h_i^{(l+1)}\).
num_rels (int) – Number of relations. .
regularizer (str) – Which weight regularizer to use “basis” or “bdd”. “basis” is short for basis-diagonal-decomposition. “bdd” is short for block-diagonal-decomposition.
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: True.
low_mem (bool, optional) – True to use low memory implementation of relation message passing function. Default: False. This option trades speed with memory consumption, and will slowdown the forward/backward. Turn it on when you encounter OOM problem during training or evaluation. Default: False.
dropout (float, optional) – Dropout rate. Default: 0.0
layer_norm (float, optional) – Add layer norm. Default: False

Examples

>>> import dgl
>>> import numpy as np
>>> import torch as th
>>> from dgl.nn import RelGraphConv
>>>
>>> g = dgl.graph(([0,1,2,3,2,5], [1,2,3,4,0,3]))
>>> feat = th.ones(6, 10)
>>> conv = RelGraphConv(10, 2, 3, regularizer='basis', num_bases=2)
>>> conv.weight.shape
torch.Size([2, 10, 2])
>>> etype = th.tensor(np.array([0,1,2,0,1,2]).astype(np.int64))
>>> res = conv(g, feat, etype)
>>> res
tensor([[ 0.3996, -2.3303],
        [-0.4323, -0.1440],
        [ 0.3996, -2.3303],
        [ 2.1046, -2.8654],
        [-0.4323, -0.1440],
        [-0.1309, -1.0000]], grad_fn=<AddBackward0>)

>>> # One-hot input
>>> one_hot_feat = th.tensor(np.array([0,1,2,3,4,5]).astype(np.int64))
>>> res = conv(g, one_hot_feat, etype)
>>> res
tensor([[ 0.5925,  0.0985],
        [-0.3953,  0.8408],
        [-0.9819,  0.5284],
        [-1.0085, -0.1721],
        [ 0.5962,  1.2002],
        [ 0.0365, -0.3532]], grad_fn=<AddBackward0>)

forward(g, feat, etypes, norm=None)[source]¶

Forward computation

Parameters

g (DGLGraph) – The graph.
feat (torch.Tensor) –
Input node features. Could be either
- \((|V|, D)\) dense tensor
- \((|V|,)\) int64 vector, representing the categorical values of each node. It then treat the input feature as an one-hot 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.

\[H^{K} = {\sum}_{k=0}^K (D^{-1/2} A D^{-1/2})^{k} X {\Theta}_{k},\]

where \(A\) denotes the adjacency matrix, \(D_{ii} = \sum_{j=0} A_{ij}\) its diagonal degree matrix, \({\Theta}_{k}\) denotes the linear weights to sum the results of different hops together.

Parameters

in_feats (int) – Input feature size. i.e, the number of dimensions of \(X\).
out_feats (int) – Output feature size. i.e, the number of dimensions of \(H^{K}\).
k (int, optional) – Number of hops \(K\). 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¶

The learnable linear module.

Type: torch.Module

Example

>>> import dgl
>>> import numpy as np
>>> import torch as th
>>> from dgl.nn import TAGConv
>>>
>>> g = dgl.graph(([0,1,2,3,2,5], [1,2,3,4,0,3]))
>>> feat = th.ones(6, 10)
>>> conv = TAGConv(10, 2, k=2)
>>> res = conv(g, feat)
>>> res
tensor([[ 0.5490, -1.6373],
        [ 0.5490, -1.6373],
        [ 0.5490, -1.6373],
        [ 0.5513, -1.8208],
        [ 0.5215, -1.6044],
        [ 0.3304, -1.9927]], grad_fn=<AddmmBackward>)

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, allow_zero_in_degree=False)[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, or pair of ints) – Input feature size; i.e, the number of dimensions of \(h_i^{(l)}\). GATConv can be applied on homogeneous graph and unidirectional bipartite graph. If the layer is to be applied to a unidirectional bipartite graph, in_feats specifies the input feature size on both the source and destination nodes. If a scalar is given, the source and destination node feature size would take the same value.
out_feats (int) – Output feature size; i.e, the number of dimensions of \(h_i^{(l+1)}\).
num_heads (int) – Number of heads in Multi-Head 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. Defaults: 0.2.
residual (bool, optional) – If True, use residual connection. Defaults: False.
activation (callable activation function/layer or None, optional.) – If not None, applies an activation function to the updated node features. Default: None.
allow_zero_in_degree (bool, optional) – If there are 0-in-degree nodes in the graph, output for those nodes will be invalid since no message will be passed to those nodes. This is harmful for some applications causing silent performance regression. This module will raise a DGLError if it detects 0-in-degree nodes in input graph. By setting True, it will suppress the check and let the users handle it by themselves. Defaults: False.

Note

Zero in-degree nodes will lead to invalid output value. This is because no message will be passed to those nodes, the aggregation function will be appied on empty input. A common practice to avoid this is to add a self-loop for each node in the graph if it is homogeneous, which can be achieved by:

>>> g = ... # a DGLGraph
>>> g = dgl.add_self_loop(g)

Calling add_self_loop will not work for some graphs, for example, heterogeneous graph since the edge type can not be decided for self_loop edges. Set allow_zero_in_degree to True for those cases to unblock the code and handle zere-in-degree nodes manually. A common practise to handle this is to filter out the nodes with zere-in-degree when use after conv.

Examples

>>> import dgl
>>> import numpy as np
>>> import torch as th
>>> from dgl.nn import GATConv

>>> # Case 1: Homogeneous graph
>>> g = dgl.graph(([0,1,2,3,2,5], [1,2,3,4,0,3]))
>>> g = dgl.add_self_loop(g)
>>> feat = th.ones(6, 10)
>>> gatconv = GATConv(10, 2, num_heads=3)
>>> res = gatconv(g, feat)
>>> res
tensor([[[ 3.4570,  1.8634],
        [ 1.3805, -0.0762],
        [ 1.0390, -1.1479]],
        [[ 3.4570,  1.8634],
        [ 1.3805, -0.0762],
        [ 1.0390, -1.1479]],
        [[ 3.4570,  1.8634],
        [ 1.3805, -0.0762],
        [ 1.0390, -1.1479]],
        [[ 3.4570,  1.8634],
        [ 1.3805, -0.0762],
        [ 1.0390, -1.1479]],
        [[ 3.4570,  1.8634],
        [ 1.3805, -0.0762],
        [ 1.0390, -1.1479]],
        [[ 3.4570,  1.8634],
        [ 1.3805, -0.0762],
        [ 1.0390, -1.1479]]], grad_fn=<BinaryReduceBackward>)

>>> # Case 2: Unidirectional bipartite graph
>>> u = [0, 1, 0, 0, 1]
>>> v = [0, 1, 2, 3, 2]
>>> g = dgl.bipartite((u, v))
>>> u_feat = th.tensor(np.random.rand(2, 5).astype(np.float32))
>>> v_feat = th.tensor(np.random.rand(4, 10).astype(np.float32))
>>> gatconv = GATConv((5,10), 2, 3)
>>> res = gatconv(g, (u_feat, v_feat))
>>> res
tensor([[[-0.6066,  1.0268],
        [-0.5945, -0.4801],
        [ 0.1594,  0.3825]],
        [[ 0.0268,  1.0783],
        [ 0.5041, -1.3025],
        [ 0.6568,  0.7048]],
        [[-0.2688,  1.0543],
        [-0.0315, -0.9016],
        [ 0.3943,  0.5347]],
        [[-0.6066,  1.0268],
        [-0.5945, -0.4801],
        [ 0.1594,  0.3825]]], grad_fn=<BinaryReduceBackward>)

forward(graph, feat)[source]¶

Compute graph attention network layer.

Parameters

graph (DGLGraph) – The graph.
feat (torch.Tensor or pair of torch.Tensor) – If a torch.Tensor is given, the input feature of shape \((N, D_{in})\) where \(D_{in}\) is size of input feature, \(N\) is the number of nodes. If a pair of torch.Tensor is given, the pair must contain two tensors of shape \((N_{in}, D_{in_{src}})\) and \((N_{out}, D_{in_{dst}})\).

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

Raises

DGLError – If there are 0-in-degree nodes in the input graph, it will raise DGLError since no message will be passed to those nodes. This will cause invalid output. The error can be ignored by setting allow_zero_in_degree parameter to True.

EdgeConv¶

class dgl.nn.pytorch.conv.EdgeConv(in_feat, out_feat, batch_norm=False, allow_zero_in_degree=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:

\[h_i^{(l+1)} = \max_{j \in \mathcal{N}(i)} \mathrm{ReLU}( \Theta \cdot (h_j^{(l)} - h_i^{(l)}) + \Phi \cdot h_i^{(l)})\]

where \(\mathcal{N}(i)\) is the neighbor of \(i\). \(\Theta\) and \(\Phi\) are linear layers.

Parameters

in_feat (int) – Input feature size; i.e, the number of dimensions of \(h_j^{(l)}\).
out_feat (int) – Output feature size; i.e., the number of dimensions of \(h_i^{(l+1)}\).
batch_norm (bool) – Whether to include batch normalization on messages. Default: False.
allow_zero_in_degree (bool, optional) – If there are 0-in-degree nodes in the graph, output for those nodes will be invalid since no message will be passed to those nodes. This is harmful for some applications causing silent performance regression. This module will raise a DGLError if it detects 0-in-degree nodes in input graph. By setting True, it will suppress the check and let the users handle it by themselves. Default: False.

Note

Zero in-degree nodes will lead to invalid output value. This is because no message will be passed to those nodes, the aggregation function will be appied on empty input. A common practice to avoid this is to add a self-loop for each node in the graph if it is homogeneous, which can be achieved by:

>>> g = ... # a DGLGraph
>>> g = dgl.add_self_loop(g)

Calling add_self_loop will not work for some graphs, for example, heterogeneous graph since the edge type can not be decided for self_loop edges. Set allow_zero_in_degree to True for those cases to unblock the code and handle zere-in-degree nodes manually. A common practise to handle this is to filter out the nodes with zere-in-degree when use after conv.

Examples

>>> import dgl
>>> import numpy as np
>>> import torch as th
>>> from dgl.nn import EdgeConv

>>> # Case 1: Homogeneous graph
>>> g = dgl.graph(([0,1,2,3,2,5], [1,2,3,4,0,3]))
>>> g = dgl.add_self_loop(g)
>>> feat = th.ones(6, 10)
>>> conv = EdgeConv(10, 2)
>>> res = conv(g, feat)
>>> res
tensor([[-0.2347,  0.5849],
        [-0.2347,  0.5849],
        [-0.2347,  0.5849],
        [-0.2347,  0.5849],
        [-0.2347,  0.5849],
        [-0.2347,  0.5849]], grad_fn=<CopyReduceBackward>)

>>> # Case 2: Unidirectional bipartite graph
>>> u = [0, 1, 0, 0, 1]
>>> v = [0, 1, 2, 3, 2]
>>> g = dgl.bipartite((u, v))
>>> u_fea = th.rand(2, 5)
>>> v_fea = th.rand(4, 5)
>>> conv = EdgeConv(5, 2, 3)
>>> res = conv(g, (u_fea, v_fea))
>>> res
tensor([[ 1.6375,  0.2085],
        [-1.1925, -1.2852],
        [ 0.2101,  1.3466],
        [ 0.2342, -0.9868]], grad_fn=<CopyReduceBackward>)

forward(g, feat)[source]¶

Forward computation

Parameters

g (DGLGraph) – The graph.
feat (Tensor or pair of tensors) –
\((N, D)\) where \(N\) is the number of nodes and \(D\) is the number of feature dimensions.

If a pair of tensors is given, the graph must be a uni-bipartite graph with only one edge type, and the two tensors must have the same dimensionality on all except the first axis.

Returns

New node features.

Return type

torch.Tensor

Raises

DGLError – If there are 0-in-degree nodes in the input graph, it will raise DGLError since no message will be passed to those nodes. This will cause invalid output. The error can be ignored by setting allow_zero_in_degree parameter to True.

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.

\[ \begin{align}\begin{aligned}h_{\mathcal{N}(i)}^{(l+1)} &= \mathrm{aggregate} \left(\{h_{j}^{l}, \forall j \in \mathcal{N}(i) \}\right)\\h_{i}^{(l+1)} &= \sigma \left(W \cdot \mathrm{concat} (h_{i}^{l}, h_{\mathcal{N}(i)}^{l+1}) \right)\\h_{i}^{(l+1)} &= \mathrm{norm}(h_{i}^{l})\end{aligned}\end{align} \]

Parameters

in_feats (int, or pair of ints) –
Input feature size; i.e, the number of dimensions of \(h_i^{(l)}\).

SAGEConv can be applied on homogeneous graph and unidirectional bipartite graph. If the layer applies on a unidirectional bipartite graph, in_feats specifies the input feature size on both the source and destination nodes. If a scalar is given, the source and destination node feature size would take the same value.

If aggregator type is gcn, the feature size of source and destination nodes are required to be the same.
out_feats (int) – Output feature size; i.e, the number of dimensions of \(h_i^{(l+1)}\).
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.

Examples

>>> import dgl
>>> import numpy as np
>>> import torch as th
>>> from dgl.nn import SAGEConv

>>> # Case 1: Homogeneous graph
>>> g = dgl.graph(([0,1,2,3,2,5], [1,2,3,4,0,3]))
>>> g = dgl.add_self_loop(g)
>>> feat = th.ones(6, 10)
>>> conv = SAGEConv(10, 2, 'pool')
>>> res = conv(g, feat)
>>> res
tensor([[-1.0888, -2.1099],
        [-1.0888, -2.1099],
        [-1.0888, -2.1099],
        [-1.0888, -2.1099],
        [-1.0888, -2.1099],
        [-1.0888, -2.1099]], grad_fn=<AddBackward0>)

>>> # Case 2: Unidirectional bipartite graph
>>> u = [0, 1, 0, 0, 1]
>>> v = [0, 1, 2, 3, 2]
>>> g = dgl.bipartite((u, v))
>>> u_fea = th.rand(2, 5)
>>> v_fea = th.rand(4, 10)
>>> conv = SAGEConv((5, 10), 2, 'mean')
>>> res = conv(g, (u_fea, v_fea))
>>> res
tensor([[ 0.3163,  3.1166],
        [ 0.3866,  2.5398],
        [ 0.5873,  1.6597],
        [-0.2502,  2.8068]], grad_fn=<AddBackward0>)

forward(graph, feat)[source]¶

Compute GraphSAGE layer.

Parameters

graph (DGLGraph) – The graph.
feat (torch.Tensor or pair of torch.Tensor) – If a torch.Tensor is given, it represents the input feature of shape \((N, D_{in})\) where \(D_{in}\) is size of input feature, \(N\) is the number of nodes. If a pair of torch.Tensor is given, the pair must contain two tensors of shape \((N_{in}, D_{in_{src}})\) and \((N_{out}, D_{in_{dst}})\).

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, allow_zero_in_degree=False)[source]¶

Bases: torch.nn.modules.module.Module

Simplifying Graph Convolution layer from paper Simplifying Graph Convolutional Networks.

\[H^{K} = (\tilde{D}^{-1/2} \tilde{A} \tilde{D}^{-1/2})^K X \Theta\]

where \(\tilde{A}\) is \(A\) + \(I\). Thus the graph input is expected to have self-loop edges added.

Parameters

in_feats (int) – Number of input features; i.e, the number of dimensions of \(X\).
out_feats (int) – Number of output features; i.e, the number of dimensions of \(H^{K}\).
k (int) – Number of hops \(K\). Defaults:1.
cached (bool) –
If True, the module would cache

\[(\tilde{D}^{-\frac{1}{2}}\tilde{A}\tilde{D}^{-\frac{1}{2}})^K X\Theta\]

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. Default: False.
allow_zero_in_degree (bool, optional) – If there are 0-in-degree nodes in the graph, output for those nodes will be invalid since no message will be passed to those nodes. This is harmful for some applications causing silent performance regression. This module will raise a DGLError if it detects 0-in-degree nodes in input graph. By setting True, it will suppress the check and let the users handle it by themselves. Default: False.

Note

Zero in-degree nodes will lead to invalid output value. This is because no message will be passed to those nodes, the aggregation function will be appied on empty input. A common practice to avoid this is to add a self-loop for each node in the graph if it is homogeneous, which can be achieved by:

>>> g = ... # a DGLGraph
>>> g = dgl.add_self_loop(g)

Calling add_self_loop will not work for some graphs, for example, heterogeneous graph since the edge type can not be decided for self_loop edges. Set allow_zero_in_degree to True for those cases to unblock the code and handle zere-in-degree nodes manually. A common practise to handle this is to filter out the nodes with zere-in-degree when use after conv.

Example

>>> import dgl
>>> import numpy as np
>>> import torch as th
>>> from dgl.nn import SGConv
>>>
>>> g = dgl.graph(([0,1,2,3,2,5], [1,2,3,4,0,3]))
>>> g = dgl.add_self_loop(g)
>>> feat = th.ones(6, 10)
>>> conv = SGConv(10, 2, k=2, cached=True)
>>> res = conv(g, feat)
>>> res
tensor([[-1.9441, -0.9343],
        [-1.9441, -0.9343],
        [-1.9441, -0.9343],
        [-2.7709, -1.3316],
        [-1.9297, -0.9273],
        [-1.9441, -0.9343]], grad_fn=<AddmmBackward>)

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

Raises

DGLError – If there are 0-in-degree nodes in the input graph, it will raise DGLError since no message will be passed to those nodes. This will cause invalid output. The error can be ignored by setting allow_zero_in_degree parameter to True.

Note

If cache is set to True, feat and graph 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.

\[ \begin{align}\begin{aligned}H^{0} &= X\\H^{l+1} &= (1-\alpha)\left(\tilde{D}^{-1/2} \tilde{A} \tilde{D}^{-1/2} H^{l}\right) + \alpha H^{0}\end{aligned}\end{align} \]

where \(\tilde{A}\) is \(A\) + \(I\).

Parameters

k (int) – The number of iterations \(K\).
alpha (float) – The teleport probability \(\alpha\).
edge_drop (float, optional) – The dropout rate on edges that controls the messages received by each node. Default: 0.

Example

>>> import dgl
>>> import numpy as np
>>> import torch as th
>>> from dgl.nn import APPNPConv
>>>
>>> g = dgl.graph(([0,1,2,3,2,5], [1,2,3,4,0,3]))
>>> feat = th.ones(6, 10)
>>> conv = APPNPConv(k=3, alpha=0.5)
>>> res = conv(g, feat)
>>> res
tensor([[1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000,
        1.0000],
        [1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000,
        1.0000],
        [1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000,
        1.0000],
        [1.0303, 1.0303, 1.0303, 1.0303, 1.0303, 1.0303, 1.0303, 1.0303, 1.0303,
        1.0303],
        [0.8643, 0.8643, 0.8643, 0.8643, 0.8643, 0.8643, 0.8643, 0.8643, 0.8643,
        0.8643],
        [0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000,
        0.5000]])

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 or mean).
init_eps (float, optional) – Initial \(\epsilon\) value, default: 0.
learn_eps (bool, optional) – If True, \(\epsilon\) will be a learnable parameter. Default: False.

Example

>>> import dgl
>>> import numpy as np
>>> import torch as th
>>> from dgl.nn import GINConv
>>>
>>> g = dgl.graph(([0,1,2,3,2,5], [1,2,3,4,0,3]))
>>> feat = th.ones(6, 10)
>>> lin = th.nn.Linear(10, 10)
>>> conv = GINConv(lin, 'max')
>>> res = conv(g, feat)
>>> res
tensor([[-0.4821,  0.0207, -0.7665,  0.5721, -0.4682, -0.2134, -0.5236,  1.2855,
        0.8843, -0.8764],
        [-0.4821,  0.0207, -0.7665,  0.5721, -0.4682, -0.2134, -0.5236,  1.2855,
        0.8843, -0.8764],
        [-0.4821,  0.0207, -0.7665,  0.5721, -0.4682, -0.2134, -0.5236,  1.2855,
        0.8843, -0.8764],
        [-0.4821,  0.0207, -0.7665,  0.5721, -0.4682, -0.2134, -0.5236,  1.2855,
        0.8843, -0.8764],
        [-0.4821,  0.0207, -0.7665,  0.5721, -0.4682, -0.2134, -0.5236,  1.2855,
        0.8843, -0.8764],
        [-0.1804,  0.0758, -0.5159,  0.3569, -0.1408, -0.1395, -0.2387,  0.7773,
        0.5266, -0.4465]], grad_fn=<AddmmBackward>)

forward(graph, feat)[source]¶

Compute Graph Isomorphism Network layer.

Parameters

graph (DGLGraph) – The graph.
feat (torch.Tensor or pair of torch.Tensor) – If a torch.Tensor is given, the input feature of shape \((N, D_{in})\) where \(D_{in}\) is size of input feature, \(N\) is the number of nodes. If a pair of torch.Tensor is given, the pair must contain two tensors of shape \((N_{in}, D_{in})\) and \((N_{out}, D_{in})\). If apply_func is not None, \(D_{in}\) should fit the input dimensionality requirement of apply_func.

Returns

The output feature of shape \((N, D_{out})\) where \(D_{out}\) is the output dimensionality of apply_func. If apply_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

in_feats (int) – Input feature size; i.e, the number of dimensions of \(x_i\).
out_feats (int) – Output feature size; i.e., the number of dimensions of \(h_i^{(t+1)}\).
n_steps (int) – Number of recurrent steps; i.e, the \(t\) in the above formula.
n_etypes (int) – Number of edge types.
bias (bool) – If True, adds a learnable bias to the output. Default: True.

Example

>>> import dgl
>>> import numpy as np
>>> import torch as th
>>> from dgl.nn import GatedGraphConv
>>>
>>> g = dgl.graph(([0,1,2,3,2,5], [1,2,3,4,0,3]))
>>> feat = th.ones(6, 10)
>>> conv = GatedGraphConv(10, 10, 2, 3)
>>> etype = th.tensor([0,1,2,0,1,2])
>>> res = conv(g, feat, etype)
>>> res
tensor([[ 0.4652,  0.4458,  0.5169,  0.4126,  0.4847,  0.2303,  0.2757,  0.7721,
        0.0523,  0.0857],
        [ 0.0832,  0.1388, -0.5643,  0.7053, -0.2524, -0.3847,  0.7587,  0.8245,
        0.9315,  0.4063],
        [ 0.6340,  0.4096,  0.7692,  0.2125,  0.2106,  0.4542, -0.0580,  0.3364,
        -0.1376,  0.4948],
        [ 0.5551,  0.7946,  0.6220,  0.8058,  0.5711,  0.3063, -0.5454,  0.2272,
        -0.6931, -0.1607],
        [ 0.2644,  0.2469, -0.6143,  0.6008, -0.1516, -0.3781,  0.5878,  0.7993,
        0.9241,  0.1835],
        [ 0.6393,  0.3447,  0.3893,  0.4279,  0.3342,  0.3809,  0.0406,  0.5030,
        0.1342,  0.0425]], grad_fn=<AddBackward0>)

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, allow_zero_in_degree=False)[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.

\[ \begin{align}\begin{aligned}u_{ij} &= f(x_i, x_j), x_j \in \mathcal{N}(i)\\w_k(u) &= \exp\left(-\frac{1}{2}(u-\mu_k)^T \Sigma_k^{-1} (u - \mu_k)\right)\\h_i^{l+1} &= \mathrm{aggregate}\left(\left\{\frac{1}{K} \sum_{k}^{K} w_k(u_{ij}), \forall j\in \mathcal{N}(i)\right\}\right)\end{aligned}\end{align} \]

where \(u\) denotes the pseudo-coordinates between a vertex and one of its neighbor, computed using function \(f\), \(\Sigma_k^{-1}\) and \(\mu_k\) are learnable parameters representing the covariance matrix and mean vector of a Gaussian kernel.

Parameters

in_feats (int) – Number of input features; i.e., the number of dimensions of \(x_i\).
out_feats (int) – Number of output features; i.e., the number of dimensions of \(h_i^{(l+1)}\).
dim (int) – Dimensionality of pseudo-coordinte; i.e, the number of dimensions of \(u_{ij}\).
n_kernels (int) – Number of kernels \(K\).
aggregator_type (str) – Aggregator type (sum, mean, max). Default: sum.
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.
allow_zero_in_degree (bool, optional) – If there are 0-in-degree nodes in the graph, output for those nodes will be invalid since no message will be passed to those nodes. This is harmful for some applications causing silent performance regression. This module will raise a DGLError if it detects 0-in-degree nodes in input graph. By setting True, it will suppress the check and let the users handle it by themselves. Default: False.

Note

Zero in-degree nodes will lead to invalid output value. This is because no message will be passed to those nodes, the aggregation function will be appied on empty input. A common practice to avoid this is to add a self-loop for each node in the graph if it is homogeneous, which can be achieved by:

>>> g = ... # a DGLGraph
>>> g = dgl.add_self_loop(g)

Calling add_self_loop will not work for some graphs, for example, heterogeneous graph since the edge type can not be decided for self_loop edges. Set allow_zero_in_degree to True for those cases to unblock the code and handle zere-in-degree nodes manually. A common practise to handle this is to filter out the nodes with zere-in-degree when use after conv.

Examples

>>> import dgl
>>> import numpy as np
>>> import torch as th
>>> from dgl.nn import GMMConv

>>> # Case 1: Homogeneous graph
>>> g = dgl.graph(([0,1,2,3,2,5], [1,2,3,4,0,3]))
>>> g = dgl.add_self_loop(g)
>>> feat = th.ones(6, 10)
>>> conv = GMMConv(10, 2, 3, 2, 'mean')
>>> pseudo = th.ones(12, 3)
>>> res = conv(g, feat, pseudo)
>>> res
tensor([[-0.3462, -0.2654],
        [-0.3462, -0.2654],
        [-0.3462, -0.2654],
        [-0.3462, -0.2654],
        [-0.3462, -0.2654],
        [-0.3462, -0.2654]], grad_fn=<AddBackward0>)

>>> # Case 2: Unidirectional bipartite graph
>>> u = [0, 1, 0, 0, 1]
>>> v = [0, 1, 2, 3, 2]
>>> g = dgl.bipartite((u, v))
>>> u_fea = th.rand(2, 5)
>>> v_fea = th.rand(4, 10)
>>> pseudo = th.ones(5, 3)
>>> conv = GMMConv((10, 5), 2, 3, 2, 'mean')
>>> res = conv(g, (u_fea, v_fea), pseudo)
>>> res
tensor([[-0.1107, -0.1559],
        [-0.1646, -0.2326],
        [-0.1377, -0.1943],
        [-0.1107, -0.1559]], grad_fn=<AddBackward0>)

forward(graph, feat, pseudo)[source]¶

Compute Gaussian Mixture Model Convolution layer.

Parameters

graph (DGLGraph) – The graph.
feat (torch.Tensor) – If a single tensor is given, the input feature of shape \((N, D_{in})\) where \(D_{in}\) is size of input feature, \(N\) is the number of nodes. If a pair of tensors are given, the pair must contain two tensors of shape \((N_{in}, D_{in_{src}})\) and \((N_{out}, D_{in_{dst}})\).
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

Raises

DGLError – If there are 0-in-degree nodes in the input graph, it will raise DGLError since no message will be passed to those nodes. This will cause invalid output. The error can be ignored by setting allow_zero_in_degree parameter to True.

ChebConv¶

class dgl.nn.pytorch.conv.ChebConv(in_feats, out_feats, k, activation=<function relu>, 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.

\[ \begin{align}\begin{aligned}h_i^{l+1} &= \sum_{k=0}^{K-1} W^{k, l}z_i^{k, l}\\Z^{0, l} &= H^{l}\\Z^{1, l} &= \tilde{L} \cdot H^{l}\\Z^{k, l} &= 2 \cdot \tilde{L} \cdot Z^{k-1, l} - Z^{k-2, l}\\\tilde{L} &= 2\left(I - \tilde{D}^{-1/2} \tilde{A} \tilde{D}^{-1/2}\right)/\lambda_{max} - I\end{aligned}\end{align} \]

where \(\tilde{A}\) is \(A\) + \(I\), \(W\) is learnable weight.

Parameters

in_feats (int) – Dimension of input features; i.e, the number of dimensions of \(h_i^{(l)}\).
out_feats (int) – Dimension of output features \(h_i^{(l+1)}\).
k (int) – Chebyshev filter size \(K\).
activation (function, optional) – Activation function. Default ReLu.
bias (bool, optional) – If True, adds a learnable bias to the output. Default: True.

Example

>>> import dgl
>>> import numpy as np
>>> import torch as th
>>> from dgl.nn import ChebConv
>>
>>> g = dgl.graph(([0,1,2,3,2,5], [1,2,3,4,0,3]))
>>> feat = th.ones(6, 10)
>>> conv = ChebConv(10, 2, 2)
>>> res = conv(g, feat)
>>> res
tensor([[ 0.6163, -0.1809],
        [ 0.6163, -0.1809],
        [ 0.6163, -0.1809],
        [ 0.9698, -1.5053],
        [ 0.3664,  0.7556],
        [-0.2370,  3.0164]], grad_fn=<AddBackward0>)

forward(graph, feat, lambda_max=None)[source]¶

Compute ChebNet 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.
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 calling dgl.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, allow_zero_in_degree=False)[source]¶

Bases: torch.nn.modules.module.Module

Attention-based Graph Neural Network layer from paper Attention-based Graph Neural Network for Semi-Supervised Learning.

\[H^{l+1} = P H^{l}\]

where \(P\) is computed as:

\[P_{ij} = \mathrm{softmax}_i ( \beta \cdot \cos(h_i^l, h_j^l))\]

where \(\beta\) is a single scalar parameter.

Parameters

init_beta (float, optional) – The \(\beta\) in the formula, a single scalar parameter.
learn_beta (bool, optional) – If True, \(\beta\) will be learnable parameter.
allow_zero_in_degree (bool, optional) – If there are 0-in-degree nodes in the graph, output for those nodes will be invalid since no message will be passed to those nodes. This is harmful for some applications causing silent performance regression. This module will raise a DGLError if it detects 0-in-degree nodes in input graph. By setting True, it will suppress the check and let the users handle it by themselves. Default: False.

Note

Zero in-degree nodes will lead to invalid output value. This is because no message will be passed to those nodes, the aggregation function will be appied on empty input. A common practice to avoid this is to add a self-loop for each node in the graph if it is homogeneous, which can be achieved by:

>>> g = ... # a DGLGraph
>>> g = dgl.add_self_loop(g)

Calling add_self_loop will not work for some graphs, for example, heterogeneous graph since the edge type can not be decided for self_loop edges. Set allow_zero_in_degree to True for those cases to unblock the code and handle zere-in-degree nodes manually. A common practise to handle this is to filter out the nodes with zere-in-degree when use after conv.

Example

>>> import dgl
>>> import numpy as np
>>> import torch as th
>>> from dgl.nn import AGNNConv
>>>
>>> g = dgl.graph(([0,1,2,3,2,5], [1,2,3,4,0,3]))
>>> g = dgl.add_self_loop(g)
>>> feat = th.ones(6, 10)
>>> conv = AGNNConv()
>>> res = conv(g, feat)
>>> res
tensor([[1., 1., 1., 1., 1., 1., 1., 1., 1., 1.],
        [1., 1., 1., 1., 1., 1., 1., 1., 1., 1.],
        [1., 1., 1., 1., 1., 1., 1., 1., 1., 1.],
        [1., 1., 1., 1., 1., 1., 1., 1., 1., 1.],
        [1., 1., 1., 1., 1., 1., 1., 1., 1., 1.],
        [1., 1., 1., 1., 1., 1., 1., 1., 1., 1.]],
    grad_fn=<BinaryReduceBackward>)

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. If a pair of torch.Tensor is given, the pair must contain two tensors of shape \((N_{in}, *)\) and \((N_{out}, *)\), the \(*\) in the later tensor must equal the previous one.

Returns

The output feature of shape \((N, *)\) where \(*\) should be the same as input shape.

Return type

torch.Tensor

Raises

DGLError – If there are 0-in-degree nodes in the input graph, it will raise DGLError since no message will be passed to those nodes. This will cause invalid output. The error can be ignored by setting allow_zero_in_degree parameter to True.

NNConv¶

class dgl.nn.pytorch.conv.NNConv(in_feats, out_feats, edge_func, aggregator_type='mean', residual=False, bias=True)[source]¶

Bases: torch.nn.modules.module.Module

Graph Convolution layer introduced in Neural Message Passing for Quantum Chemistry.

\[h_{i}^{l+1} = h_{i}^{l} + \mathrm{aggregate}\left(\left\{ f_\Theta (e_{ij}) \cdot h_j^{l}, j\in \mathcal{N}(i) \right\}\right)\]

where \(e_{ij}\) is the edge feature, \(f_\Theta\) is a function with learnable parameters.

Parameters

in_feats (int) – Input feature size; i.e, the number of dimensions of \(h_j^{(l)}\). NNConv can be applied on homogeneous graph and unidirectional bipartite graph. If the layer is to be applied on a unidirectional bipartite graph, in_feats specifies the input feature size on both the source and destination nodes. If a scalar is given, the source and destination node feature size would take the same value.
out_feats (int) – Output feature size; i.e., the number of dimensions of \(h_i^{(l+1)}\).
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 or max).
residual (bool, optional) – If True, use residual connection. Default: False.
bias (bool, optional) – If True, adds a learnable bias to the output. Default: True.

Examples

>>> import dgl
>>> import numpy as np
>>> import torch as th
>>> from dgl.nn import NNConv

>>> # Case 1: Homogeneous graph
>>> g = dgl.graph(([0,1,2,3,2,5], [1,2,3,4,0,3]))
>>> g = dgl.add_self_loop(g)
>>> feat = th.ones(6, 10)
>>> lin = th.nn.Linear(5, 20)
>>> def edge_func(efeat):
...     return lin(efeat)
>>> efeat = th.ones(6+6, 5)
>>> conv = NNConv(10, 2, edge_func, 'mean')
>>> res = conv(g, feat, efeat)
>>> res
tensor([[-1.5243, -0.2719],
        [-1.5243, -0.2719],
        [-1.5243, -0.2719],
        [-1.5243, -0.2719],
        [-1.5243, -0.2719],
        [-1.5243, -0.2719]], grad_fn=<AddBackward0>)

>>> # Case 2: Unidirectional bipartite graph
>>> u = [0, 1, 0, 0, 1]
>>> v = [0, 1, 2, 3, 2]
>>> g = dgl.bipartite((u, v))
>>> u_feat = th.tensor(np.random.rand(2, 10).astype(np.float32))
>>> v_feat = th.tensor(np.random.rand(4, 10).astype(np.float32))
>>> conv = NNConv(10, 2, edge_func, 'mean')
>>> efeat = th.ones(5, 5)
>>> res = conv(g, (u_feat, v_feat), efeat)
>>> res
tensor([[-0.6568,  0.5042],
        [ 0.9089, -0.5352],
        [ 0.1261, -0.0155],
        [-0.6568,  0.5042]], grad_fn=<AddBackward0>)

forward(graph, feat, efeat)[source]¶

Compute MPNN Graph Convolution layer.

Parameters

graph (DGLGraph) – The graph.
feat (torch.Tensor or pair of 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_func.

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 Protein-Ligand Binding Affinity.

Denoting 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 \(t\):

\[p_{i, t}^{k} = \sum_{j\in N(i)} e_{ij}^{k} * 1(z_j == t)\]

Then concatenate the results for all RBF kernels and atom types.

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.

Note

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.

Example

>>> import dgl
>>> import numpy as np
>>> import torch as th
>>> from dgl.nn import AtomicConv

>>> g = dgl.graph(([0,1,2,3,2,5], [1,2,3,4,0,3]))
>>> feat = th.ones(6, 1)
>>> edist = th.ones(6, 1)
>>> interaction_cutoffs = th.ones(3).float() * 2
>>> rbf_kernel_means = th.ones(3).float()
>>> rbf_kernel_scaling = th.ones(3).float()
>>> conv = AtomicConv(interaction_cutoffs, rbf_kernel_means, rbf_kernel_scaling)
>>> res = conv(g, feat, edist)
>>> res
tensor([[0.5000, 0.5000, 0.5000],
            [0.5000, 0.5000, 0.5000],
            [0.5000, 0.5000, 0.5000],
            [1.0000, 1.0000, 1.0000],
            [0.5000, 0.5000, 0.5000],
            [0.0000, 0.0000, 0.0000]], grad_fn=<ViewBackward>)

forward(graph, feat, distances)[source]¶

Apply the atomic convolution layer.

Parameters

graph (DGLGraph) – 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 \((V, K * T)\)

CFConv¶

class dgl.nn.pytorch.conv.CFConv(node_in_feats, edge_in_feats, hidden_feats, out_feats)[source]¶

Bases: torch.nn.modules.module.Module

CFConv in SchNet.

SchNet is introduced in SchNet: A continuous-filter convolutional neural network for modeling quantum interactions.

It combines node and edge features in message passing and updates node representations.

\[h_i^{(l+1)} = \sum_{j\in \mathcal{N}(i)} h_j^{l} \circ W^{(l)}e_ij\]

where \(\circ\) represents element-wise multiplication and for \(\text{SPP}\) :

\[\text{SSP}(x) = \frac{1}{\beta} * \log(1 + \exp(\beta * x)) - \log(\text{shift})\]

Parameters

node_in_feats (int) – Size for the input node features \(h_j^{(l)}\).
edge_in_feats (int) – Size for the input edge features \(e_ij\).
hidden_feats (int) – Size for the hidden representations.
out_feats (int) – Size for the output representations \(h_j^{(l+1)}\).

Example

>>> import dgl
>>> import numpy as np
>>> import torch as th
>>> from dgl.nn import CFConv
>>> g = dgl.graph(([0,1,2,3,2,5], [1,2,3,4,0,3]))
>>> nfeat = th.ones(6, 10)
>>> efeat = th.ones(6, 5)
>>> conv = CFConv(10, 5, 3, 2)
>>> res = conv(g, nfeat, efeat)
>>> res
tensor([[-0.1209, -0.2289],
        [-0.1209, -0.2289],
        [-0.1209, -0.2289],
        [-0.1135, -0.2338],
        [-0.1209, -0.2289],
        [-0.1283, -0.2240]], grad_fn=<SubBackward0>)

forward(g, node_feats, edge_feats)[source]¶

Performs message passing and updates node representations.

Parameters

g (DGLGraph) – The graph.
node_feats (float32 tensor of shape (V, node_in_feats)) – Input node features, V for the number of nodes.
edge_feats (float32 tensor of shape (E, edge_in_feats)) – Input edge features, E for the number of edges.

Returns

Updated node representations.

Return type

float32 tensor of shape (V, out_feats)

DotGatConv¶

class dgl.nn.pytorch.conv.DotGatConv(in_feats, out_feats, allow_zero_in_degree=False)[source]¶

Bases: torch.nn.modules.module.Module

Apply dot product version of self attention in GCN.

\[h_i^{(l+1)} = \sum_{j\in \mathcal{N}(i)} \alpha_{i, j} h_j^{(l)}\]

where \(\alpha_{ij}\) is the attention score bewteen node \(i\) and node \(j\):

\[ \begin{align}\begin{aligned}\alpha_{i, j} &= \mathrm{softmax_i}(e_{ij}^{l})\\e_{ij}^{l} &= ({W_i^{(l)} h_i^{(l)}})^T \cdot {W_j^{(l)} h_j^{(l)}}\end{aligned}\end{align} \]

where \(W_i\) and \(W_j\) transform node \(i\)’s and node \(j\)’s features into the same dimension, so that when compute note features’ similarity, it can use dot-product.

Parameters

in_feats (int, or pair of ints) – Input feature size; i.e, the number of dimensions of \(h_i^{(l)}\). DotGatConv can be applied on homogeneous graph and unidirectional bipartite graph. If the layer is to be applied to a unidirectional bipartite graph, in_feats specifies the input feature size on both the source and destination nodes. If a scalar is given, the source and destination node feature size would take the same value.
out_feats (int) – Output feature size; i.e, the number of dimensions of \(h_i^{(l+1)}\).
allow_zero_in_degree (bool, optional) – If there are 0-in-degree nodes in the graph, output for those nodes will be invalid since no message will be passed to those nodes. This is harmful for some applications causing silent performance regression. This module will raise a DGLError if it detects 0-in-degree nodes in input graph. By setting True, it will suppress the check and let the users handle it by themselves. Default: False.

Note

Zero in-degree nodes will lead to invalid output value. This is because no message will be passed to those nodes, the aggregation function will be appied on empty input. A common practice to avoid this is to add a self-loop for each node in the graph if it is homogeneous, which can be achieved by:

>>> g = ... # a DGLGraph
>>> g = dgl.add_self_loop(g)

Calling add_self_loop will not work for some graphs, for example, heterogeneous graph since the edge type can not be decided for self_loop edges. Set allow_zero_in_degree to True for those cases to unblock the code and handle zere-in-degree nodes manually. A common practise to handle this is to filter out the nodes with zere-in-degree when use after conv.

Examples

>>> import dgl
>>> import numpy as np
>>> import torch as th
>>> from dgl.nn import DotGatConv

>>> # Case 1: Homogeneous graph
>>> g = dgl.graph(([0,1,2,3,2,5], [1,2,3,4,0,3]))
>>> g = dgl.add_self_loop(g)
>>> feat = th.ones(6, 10)
>>> gatconv = DotGatConv(10, 2)
>>> res = gatconv(g, feat)
>>> res
tensor([[-0.6958, -0.8752],
        [-0.6958, -0.8752],
        [-0.6958, -0.8752],
        [-0.6958, -0.8752],
        [-0.6958, -0.8752],
        [-0.6958, -0.8752]], grad_fn=<CopyReduceBackward>)

>>> # Case 2: Unidirectional bipartite graph
>>> u = [0, 1, 0, 0, 1]
>>> v = [0, 1, 2, 3, 2]
>>> g = dgl.bipartite((u, v))
>>> u_feat = th.tensor(np.random.rand(2, 5).astype(np.float32))
>>> v_feat = th.tensor(np.random.rand(4, 10).astype(np.float32))
>>> gatconv = DotGatConv((5,10), 2)
>>> res = gatconv(g, (u_feat, v_feat))
>>> res
tensor([[ 0.4718,  0.0864],
        [ 0.7099, -0.0335],
        [ 0.5869,  0.0284],
        [ 0.4718,  0.0864]], grad_fn=<CopyReduceBackward>)

forward(graph, feat)[source]¶

Apply dot product version of self attention in GCN.

Parameters

graph (DGLGraph or bi_partities graph) – The graph
feat (torch.Tensor or pair of torch.Tensor) – If a torch.Tensor is given, the input feature of shape \((N, D_{in})\) where \(D_{in}\) is size of input feature, \(N\) is the number of nodes. If a pair of torch.Tensor is given, the pair must contain two tensors of shape \((N_{in}, D_{in_{src}})\) and \((N_{out}, D_{in_{dst}})\).

Returns

The output feature of shape \((N, D_{out})\) where \(D_{out}\) is size of output feature.

Return type

torch.Tensor

Raises

DGLError – If there are 0-in-degree nodes in the input graph, it will raise DGLError since no message will be passed to those nodes. This will cause invalid output. The error can be ignored by setting allow_zero_in_degree parameter to True.

Dense Conv Layers¶

DenseGraphConv¶

class dgl.nn.pytorch.conv.DenseGraphConv(in_feats, out_feats, norm='both', 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; i.e, the number of dimensions of \(h_j^{(l)}\).
out_feats (int) – Output feature size; i.e., the number of dimensions of \(h_i^{(l+1)}\).
norm (str, optional) – How to apply the normalizer. If is ‘right’, divide the aggregated messages by each node’s in-degrees, which is equivalent to averaging the received messages. If is ‘none’, no normalization is applied. Default is ‘both’, where the \(c_{ij}\) in the paper is applied.
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.

Notes

Zero in-degree nodes will lead to all-zero output. A common practice to avoid this is to add a self-loop for each node in the graph, which can be achieved by setting the diagonal of the adjacency matrix to be 1.

Example

>>> import dgl
>>> import numpy as np
>>> import torch as th
>>> from dgl.nn import DenseGraphConv
>>>
>>> feat = th.ones(6, 10)
>>> adj = th.tensor([[0., 0., 1., 0., 0., 0.],
...         [1., 0., 0., 0., 0., 0.],
...         [0., 1., 0., 0., 0., 0.],
...         [0., 0., 1., 0., 0., 1.],
...         [0., 0., 0., 1., 0., 0.],
...         [0., 0., 0., 0., 0., 0.]])
>>> conv = DenseGraphConv(10, 2)
>>> res = conv(adj, feat)
>>> res
tensor([[0.2159, 1.9027],
        [0.3053, 2.6908],
        [0.3053, 2.6908],
        [0.3685, 3.2481],
        [0.3053, 2.6908],
        [0.0000, 0.0000]], grad_fn=<AddBackward0>)

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; i.e, the number of dimensions of \(h_i^{(l)}\).
out_feats (int) – Output feature size; i.e, the number of dimensions of \(h_i^{(l+1)}\).
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.

Example

>>> import dgl
>>> import numpy as np
>>> import torch as th
>>> from dgl.nn import DenseSAGEConv
>>>
>>> feat = th.ones(6, 10)
>>> adj = th.tensor([[0., 0., 1., 0., 0., 0.],
...         [1., 0., 0., 0., 0., 0.],
...         [0., 1., 0., 0., 0., 0.],
...         [0., 0., 1., 0., 0., 1.],
...         [0., 0., 0., 1., 0., 0.],
...         [0., 0., 0., 0., 0., 0.]])
>>> conv = DenseSAGEConv(10, 2)
>>> res = conv(adj, feat)
>>> res
tensor([[1.0401, 2.1008],
        [1.0401, 2.1008],
        [1.0401, 2.1008],
        [1.0401, 2.1008],
        [1.0401, 2.1008],
        [1.0401, 2.1008]], grad_fn=<AddmmBackward>)

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

in_feats (int) – Dimension of input features \(h_i^{(l)}\).
out_feats (int) – Dimension of output features \(h_i^{(l+1)}\).
k (int) – Chebyshev filter size.
activation (function, optional) – Activation function, default is ReLu.
bias (bool, optional) – If True, adds a learnable bias to the output. Default: True.

Example

>>> import dgl
>>> import numpy as np
>>> import torch as th
>>> from dgl.nn import DenseChebConv
>>>
>>> feat = th.ones(6, 10)
>>> adj = th.tensor([[0., 0., 1., 0., 0., 0.],
...         [1., 0., 0., 0., 0., 0.],
...         [0., 1., 0., 0., 0., 0.],
...         [0., 0., 1., 0., 0., 1.],
...         [0., 0., 0., 1., 0., 0.],
...         [0., 0., 0., 0., 0., 0.]])
>>> conv = DenseChebConv(10, 2, 2)
>>> res = conv(adj, feat)
>>> res
tensor([[-3.3516, -2.4797],
        [-3.3516, -2.4797],
        [-3.3516, -2.4797],
        [-4.5192, -3.0835],
        [-2.5259, -2.0527],
        [-0.5327, -1.0219]], grad_fn=<AddBackward0>)

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 a graph .

\[r^{(i)} = \sum_{k=1}^{N_i} x^{(i)}_k\]

Notes

Input: Could be one graph, or a batch of graphs. If using a batch of graphs, make sure nodes in all graphs have the same feature size, and concatenate nodes’ feature together as the input.

Examples

The following example uses PyTorch backend.

>>> import dgl
>>> import torch as th
>>> from dgl.nn.pytorch.glob import SumPooling
>>>
>>> g1 = dgl.DGLGraph()
>>> g1.add_nodes(2)
>>> g1_node_feats = th.ones(2,5)
>>>
>>> g2 = dgl.DGLGraph()
>>> g2.add_nodes(3)
>>> g2_node_feats = th.ones(3,5)
>>>
>>> sumpool = SumPooling()

Case 1: Input a single graph

>>> sumpool(g1, g1_node_feats)
    tensor([[2., 2., 2., 2., 2.]])

Case 2: Input a batch of graphs

Build a batch of DGL graphs and concatenate all graphs’ node features into one tensor.

>>> batch_g = dgl.batch([g1, g2])
>>> batch_f = th.cat([g1_node_feats, g2_node_feats])
>>>
>>> sumpool(batch_g, batch_f)
    tensor([[2., 2., 2., 2., 2.],
            [3., 3., 3., 3., 3.]])

forward(graph, feat)[source]¶

Compute sum pooling.

Parameters

graph (DGLGraph) – a DGLGraph or a batch of DGLGraphs
feat (torch.Tensor) – The input feature with shape \((N, D)\), where \(N\) is the number of nodes in the graph, and \(D\) means the size of features.

Returns

The output feature with shape \((B, D)\), where \(B\) refers to the batch size of input graphs.

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 a graph.

\[r^{(i)} = \frac{1}{N_i}\sum_{k=1}^{N_i} x^{(i)}_k\]

Notes

Input: Could be one graph, or a batch of graphs. If using a batch of graphs, make sure nodes in all graphs have the same feature size, and concatenate nodes’ feature together as the input.

Examples

The following example uses PyTorch backend.

>>> import dgl
>>> import torch as th
>>> from dgl.nn.pytorch.glob import AvgPooling
>>>
>>> g1 = dgl.DGLGraph()
>>> g1.add_nodes(2)
>>> g1_node_feats = th.ones(2,5)
>>>
>>> g2 = dgl.DGLGraph()
>>> g2.add_nodes(3)
>>> g2_node_feats = th.ones(3,5)
>>>
>>> avgpool = AvgPooling()

Case 1: Input single graph

>>> avgpool(g1, g1_node_feats)
    tensor([[1., 1., 1., 1., 1.]])

Case 2: Input a batch of graphs

Build a batch of DGL graphs and concatenate all graphs’ note features into one tensor.

>>> batch_g = dgl.batch([g1, g2])
>>> batch_f = th.cat([g1_node_feats, g2_node_feats])
>>>
>>> avgpool(batch_g, batch_f)
    tensor([[1., 1., 1., 1., 1.],
            [1., 1., 1., 1., 1.]])

forward(graph, feat)[source]¶

Compute average pooling.

Parameters

graph (DGLGraph) – A DGLGraph or a batch of DGLGraphs.
feat (torch.Tensor) – The input feature with shape \((N, D)\), where \(N\) is the number of nodes in the graph, and \(D\) means the size of features.

Returns

The output feature with shape \((B, D)\), where \(B\) refers to the batch size of input graphs.

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 a graph.

\[r^{(i)} = \max_{k=1}^{N_i}\left( x^{(i)}_k \right)\]

Notes

Input: Could be one graph, or a batch of graphs. If using a batch of graphs, make sure nodes in all graphs have the same feature size, and concatenate nodes’ feature together as the input.

Examples

The following example uses PyTorch backend.

>>> import dgl
>>> import torch as th
>>> from dgl.nn.pytorch.glob import MaxPooling
>>>
>>> g1 = dgl.DGLGraph()
>>> g1.add_nodes(2)
>>> g1_node_feats = th.ones(2,5)
>>>
>>> g2 = dgl.DGLGraph()
>>> g2.add_nodes(3)
>>> g2_node_feats = th.ones(3,5)
>>>
>>> maxpool = MaxPooling()

Case 1: Input a single graph

>>> maxpool(g1, g1_node_feats)
    tensor([[1., 1., 1., 1., 1.]])

Case 2: Input a batch of graphs

Build a batch of DGL graphs and concatenate all graphs’ node features into one tensor.

>>> batch_g = dgl.batch([g1, g2])
>>> batch_f = th.cat([g1_node_feats, g2_node_feats])
>>>
>>> maxpool(batch_g, batch_f)
    tensor([[1., 1., 1., 1., 1.],
            [1., 1., 1., 1., 1.]])

forward(graph, feat)[source]¶

Compute max pooling.

Parameters

graph (DGLGraph) – A DGLGraph or a batch of DGLGraphs.
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 \((B, *)\), where \(B\) refers to the batch size.

Return type

torch.Tensor

SortPooling¶

class dgl.nn.pytorch.glob.SortPooling(k)[source]¶

Bases: torch.nn.modules.module.Module

Apply Sort Pooling (An End-to-End Deep Learning Architecture for Graph Classification) over the nodes in a graph.

Parameters: k (int) – The number of nodes to hold for each graph.

Notes

Input: Could be one graph, or a batch of graphs. If using a batch of graphs, make sure nodes in all graphs have the same feature size, and concatenate nodes’ feature together as the input.

Examples

>>> import dgl
>>> import torch as th
>>> from dgl.nn.pytorch.glob import SortPooling
>>>
>>> g1 = dgl.DGLGraph()
>>> g1.add_nodes(2)
>>> g1_node_feats = th.ones(2,5)
>>>
>>> g2 = dgl.DGLGraph()
>>> g2.add_nodes(3)
>>> g2_node_feats = th.ones(3,5)
>>>
>>> sortpool = SortPooling(k=2)

Case 1: Input a single graph

>>> sortpool(g1, g1_node_feats)
    tensor([[1., 1., 1., 1., 1., 1., 1., 1., 1., 1.]])

Case 2: Input a batch of graphs

Build a batch of DGL graphs and concatenate all graphs’ node features into one tensor.

>>> batch_g = dgl.batch([g1, g2])
>>> batch_f = th.cat([g1_node_feats, g2_node_feats])
>>>
>>> sortpool(batch_g, batch_f)
    tensor([[1., 1., 1., 1., 1., 1., 1., 1., 1., 1.],
            [1., 1., 1., 1., 1., 1., 1., 1., 1., 1.]])

forward(graph, feat)[source]¶

Compute sort pooling.

Parameters

graph (DGLGraph) – A DGLGraph or a batch of DGLGraphs.
feat (torch.Tensor) – The input feature with shape \((N, D)\), where \(N\) is the number of nodes in the graph, and \(D\) means the size of features.

Returns

The output feature with shape \((B, k * D)\), where \(B\) refers to the batch size of input graphs.

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 a 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) – A DGLGraph or a batch of DGLGraphs.
feat (torch.Tensor) – The input feature with shape \((N, D)\) where \(N\) is the number of nodes in the graph, and \(D\) means the size of features.

Returns

The output feature with shape \((B, D)\), where \(B\) refers to the batch size.

Return type

torch.Tensor

Set2Set¶

class dgl.nn.pytorch.glob.Set2Set(input_dim, n_iters, n_layers)[source]¶

Bases: torch.nn.modules.module.Module

For each individual graph in the batch, set2set computes

\[ \begin{align}\begin{aligned}q_t &= \mathrm{LSTM} (q^*_{t-1})\\\alpha_{i,t} &= \mathrm{softmax}(x_i \cdot q_t)\\r_t &= \sum_{i=1}^N \alpha_{i,t} x_i\\q^*_t &= q_t \Vert r_t\end{aligned}\end{align} \]

for this graph.

Parameters

input_dim (int) – The size of each input sample.
n_iters (int) – The number of iterations.
n_layers (int) – The number of recurrent layers.

forward(graph, feat)[source]¶

Compute set2set pooling.

Parameters

graph (DGLGraph) – The input graph.
feat (torch.Tensor) – The input feature with shape \((N, D)\) where \(N\) is the number of nodes in the graph, and \(D\) means the size of features.

Returns

The output feature with shape \((B, D)\), where \(B\) refers to the batch size, and \(D\) means the size of features.

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 Attention-based Permutation-Invariant Neural Networks.

Parameters

d_model (int) – The hidden size of the model.
n_heads (int) – The number of heads.
d_head (int) – The hidden size of each head.
d_ff (int) – The kernel size in FFN (Positionwise Feed-Forward Network) layer.
n_layers (int) – The number of layers.
block_type (str) – Building block type: ‘sab’ (Set Attention Block) or ‘isab’ (Induced Set Attention Block).
m (int or None) – The number of induced vectors in ISAB Block. Set to None if block type is ‘sab’.
dropouth (float) – The dropout rate of each sublayer.
dropouta (float) – The dropout rate of attention heads.

forward(graph, feat)[source]¶

Compute the Encoder part of Set Transformer.

Parameters

graph (DGLGraph) – The input 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 Attention-based Permutation-Invariant Neural Networks.

Parameters

d_model (int) – Hidden size of the model.
num_heads (int) – The number of heads.
d_head (int) – Hidden size of each head.
d_ff (int) – Kernel size in FFN (Positionwise Feed-Forward Network) layer.
n_layers (int) – The number of layers.
k (int) – The 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) – The input graph.
feat (torch.Tensor) – The input feature with shape \((N, D)\), where \(N\) is the number of nodes in the graph, and \(D\) means the size of features.

Returns

The output feature with shape \((B, D)\), where \(B\) refers to the batch size.

Return type

torch.Tensor

Heterogeneous Graph Convolution Module¶

HeteroGraphConv¶

class dgl.nn.pytorch.HeteroGraphConv(mods, aggregate='sum')[source]¶

Bases: torch.nn.modules.module.Module

A generic module for computing convolution on heterogeneous graphs.

The heterograph convolution applies sub-modules on their associating relation graphs, which reads the features from source nodes and writes the updated ones to destination nodes. If multiple relations have the same destination node types, their results are aggregated by the specified method.

If the relation graph has no edge, the corresponding module will not be called.

Examples

Create a heterograph with three types of relations and nodes.

>>> import dgl
>>> g = dgl.heterograph({
...     ('user', 'follows', 'user') : edges1,
...     ('user', 'plays', 'game') : edges2,
...     ('store', 'sells', 'game')  : edges3})

Create a HeteroGraphConv that applies different convolution modules to different relations. Note that the modules for 'follows' and 'plays' do not share weights.

>>> import dgl.nn.pytorch as dglnn
>>> conv = dglnn.HeteroGraphConv({
...     'follows' : dglnn.GraphConv(...),
...     'plays' : dglnn.GraphConv(...),
...     'sells' : dglnn.SAGEConv(...)},
...     aggregate='sum')

Call forward with some 'user' features. This computes new features for both 'user' and 'game' nodes.

>>> import torch as th
>>> h1 = {'user' : th.randn((g.number_of_nodes('user'), 5))}
>>> h2 = conv(g, h1)
>>> print(h2.keys())
dict_keys(['user', 'game'])

Call forward with both 'user' and 'store' features. Because both the 'plays' and 'sells' relations will update the 'game' features, their results are aggregated by the specified method (i.e., summation here).

>>> f1 = {'user' : ..., 'store' : ...}
>>> f2 = conv(g, f1)
>>> print(f2.keys())
dict_keys(['user', 'game'])

Call forward with some 'store' features. This only computes new features for 'game' nodes.

>>> g1 = {'store' : ...}
>>> g2 = conv(g, g1)
>>> print(g2.keys())
dict_keys(['game'])

Call forward with a pair of inputs is allowed and each submodule will also be invoked with a pair of inputs.

>>> x_src = {'user' : ..., 'store' : ...}
>>> x_dst = {'user' : ..., 'game' : ...}
>>> y_dst = conv(g, (x_src, x_dst))
>>> print(y_dst.keys())
dict_keys(['user', 'game'])

Parameters

mods (dict[str, nn.Module]) – Modules associated with every edge types. The forward function of each module must have a DGLHeteroGraph object as the first argument, and its second argument is either a tensor object representing the node features or a pair of tensor object representing the source and destination node features.
aggregate (str, callable, optional) –
Method for aggregating node features generated by different relations. Allowed string values are ‘sum’, ‘max’, ‘min’, ‘mean’, ‘stack’. The ‘stack’ aggregation is performed along the second dimension, whose order is deterministic. User can also customize the aggregator by providing a callable instance. For example, aggregation by summation is equivalent to the follows:
```
def my_agg_func(tensors, dsttype):
    # tensors: is a list of tensors to aggregate
    # dsttype: string name of the destination node type for which the
    #          aggregation is performed
    stacked = torch.stack(tensors, dim=0)
    return torch.sum(stacked, dim=0)
```

mods¶

Modules associated with every edge types.

Type: dict[str, nn.Module]

forward(g, inputs, mod_args=None, mod_kwargs=None)[source]¶

Forward computation

Invoke the forward function with each module and aggregate their results.

Parameters

g (DGLHeteroGraph) – Graph data.
inputs (dict[str, Tensor] or pair of dict[str, Tensor]) – Input node features.
mod_args (dict[str, tuple[any]], optional) – Extra positional arguments for the sub-modules.
mod_kwargs (dict[str, dict[str, any]], optional) – Extra key-word arguments for the sub-modules.

Returns

Output representations for every types of nodes.

Return type

dict[str, 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.

DGL supports two modes: sequentially apply GNN modules on 1) the same graph or 2) a list of given graphs. In the second case, the number of graphs equals the number of modules inside this container.

Parameters: *args – Sub-modules of torch.nn.Module that will be added to the container in the order by which they are passed in the constructor.

Examples

The following example uses PyTorch backend.

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):
>>>         with graph.local_scope():
>>>             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):
>>>         with graph.local_scope():
>>>             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]])

forward(graph, *feats)[source]¶

Sequentially apply modules to the input.

Parameters

graph (DGLGraph or list of DGLGraphs) – The graph(s) to apply modules on.
*feats – Input features. The output of the \(i\)-th module should match the input of the \((i+1)\)-th module in the sequential.

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.

The KNNGraph is implemented in the following steps:

Compute an NxN matrix of pairwise distance for all points.
Pick the k points with the smallest distance for each point as their k-nearest neighbors.
Construct a graph with edges to each point as a node from its k-nearest neighbors.

The overall computational complexity is \(O(N^2(logN + D)\).

If a batch of point sets is provided, 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 k-nearest neighbors of the corresponding point.

Parameters: k (int) – The number of neighbors.

Notes

The nearest neighbors found for a node include the node itself.

Examples

The following example uses PyTorch backend.

>>> import torch
>>> from dgl.nn.pytorch.factory import KNNGraph
>>>
>>> kg = KNNGraph(2)
>>> x = torch.tensor([[0,1],
                      [1,2],
                      [1,3],
                      [100, 101],
                      [101, 102],
                      [50, 50]])
>>> g = kg(x)
>>> print(g.edges())
    (tensor([0, 1, 1, 1, 2, 2, 2, 3, 3, 4, 4, 5]),
     tensor([0, 0, 1, 2, 1, 2, 5, 3, 4, 3, 4, 5]))

forward(x)[source]¶

Forward computation.

Parameters: x (Tensor) – \((M, D)\) or \((N, M, D)\) where \(N\) means the number of point sets, \(M\) means the number of points in each point set, and \(D\) means the size of features.
Returns: A DGLGraph without features.
Return type: DGLGraph

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 sets is provided, then the point \(j\) in the point set \(i\) is mapped to graph node ID: \(\sum_{p<i} |V_p| + j\), where \(|V_p|\) means the number of points in the point set \(p\).

The predecessors of each node are the k-nearest neighbors of the corresponding point.

Parameters: k (int) – The number of neighbors.

Notes

The nearest neighbors found for a node include the node itself.

Examples

The following example uses PyTorch backend.

>>> import torch
>>> from dgl.nn.pytorch.factory import SegmentedKNNGraph
>>>
>>> kg = SegmentedKNNGraph(2)
>>> x = torch.tensor([[0,1],
...                   [1,2],
...                   [1,3],
...                   [100, 101],
...                   [101, 102],
...                   [50, 50],
...                   [24,25],
...                   [25,24]])
>>> g = kg(x, [3,3,2])
>>> print(g.edges())
(tensor([0, 1, 1, 1, 2, 2, 3, 3, 3, 4, 4, 5, 6, 6, 7, 7]),
 tensor([0, 0, 1, 2, 1, 2, 3, 4, 5, 3, 4, 5, 6, 7, 6, 7]))
>>>

forward(x, segs)[source]¶

Forward computation.

Parameters

x (Tensor) – \((M, D)\) where \(M\) means the total number of points in all point sets, and \(D\) means the size of features.
segs (iterable of int) – \((N)\) integers where \(N\) means the number of point sets. The number of elements must sum up to \(M\). And any \(N\) should \(\ge k\)

Returns

A DGLGraph without features.

Return type

DGLGraph