NNConv

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

Bases: Module

Graph Convolution layer from 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.heterograph({('_N', '_E', '_N'):(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 \((E, *)\), which should fit the input shape requirement of edge_func. \(E\) 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

reset_parameters()[source]: Description

Reinitialize learnable parameters.

Note

The model parameters are initialized using Glorot uniform initialization and the bias is initialized to be zero.

NNConv

Description