# RelGraphConv¶

class dgl.nn.pytorch.conv.RelGraphConv(in_feat, out_feat, num_rels, regularizer=None, num_bases=None, bias=True, activation=None, self_loop=True, dropout=0.0, layer_norm=False)[source]

Bases: torch.nn.modules.module.Module

Relational graph convolution layer from Modeling Relational Data with Graph Convolutional Networks

It can be described in as below:

$h_i^{(l+1)} = \sigma(\sum_{r\in\mathcal{R}} \sum_{j\in\mathcal{N}^r(i)}e_{j,i}W_r^{(l)}h_j^{(l)}+W_0^{(l)}h_i^{(l)})$

where $$\mathcal{N}^r(i)$$ is the neighbor set of node $$i$$ w.r.t. relation $$r$$. $$e_{j,i}$$ is the normalizer. $$\sigma$$ 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, optional) –

Which weight regularizer to use (“basis”, “bdd” or None):

• ”basis” is for basis-decomposition.

• ”bdd” is for block-diagonal-decomposition.

• None applies no regularization.

Default: None.

• num_bases (int, optional) – Number of bases. It comes into effect when a regularizer is applied. If None, it uses number of relations (num_rels). Default: None. Note that in_feat and out_feat must be divisible by num_bases when applying “bdd” regularizer.

• 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.

• dropout (float, optional) – Dropout rate. Default: 0.0

• layer_norm (bool, optional) – True to 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)
>>> etype = th.tensor([0,1,2,0,1,2])
>>> 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],

forward(g, feat, etypes, norm=None, *, presorted=False)[source]

Forward computation.

Parameters
• g (DGLGraph) – The graph.

• feat (torch.Tensor) – A 2D tensor of node features. Shape: $$(|V|, D_{in})$$.

• etypes (torch.Tensor or list[int]) – An 1D integer tensor of edge types. Shape: $$(|E|,)$$.

• norm (torch.Tensor, optional) – An 1D tensor of edge norm value. Shape: $$(|E|,)$$.

• presorted (bool, optional) – Whether the edges of the input graph have been sorted by their types. Forward on pre-sorted graph may be faster. Graphs created by to_homogeneous() automatically satisfy the condition. Also see reorder_graph() for sorting edges manually.

Returns

New node features. Shape: $$(|V|, D_{out})$$.

Return type

torch.Tensor