import tensorlayerx as tlx
from gammagl.layers.conv import MessagePassing
[docs]
class GINConv(MessagePassing):
r"""The graph isomorphism operator from the `"How Powerful are
Graph Neural Networks?" <https://arxiv.org/abs/1810.00826>`_ paper
.. math::
\mathbf{x}^{\prime}_i = h_{\mathbf{\Theta}} \left( (1 + \epsilon) \cdot
\mathbf{x}_i + \sum_{j \in \mathcal{N}(i)} \mathbf{x}_j \right)
or
.. math::
\mathbf{X}^{\prime} = h_{\mathbf{\Theta}} \left( \left( \mathbf{A} +
(1 + \epsilon) \cdot \mathbf{I} \right) \cdot \mathbf{X} \right),
here :math:`h_{\mathbf{\Theta}}` denotes a neural network, *.i.e.* an MLP.
Parameters
----------
nn: tlx.nn.Module
A neural network :math:`h_{\mathbf{\Theta}}` that
maps node features :obj:`x` of shape :obj:`[-1, in_channels]` to
shape :obj:`[-1, out_channels]`, *e.g.*, defined by
:class:`torch.nn.Sequential`.
eps: float, optional
(Initial) :math:`\epsilon`-value.
(default: :obj:`0.`)
train_eps: bool, optional
If set to :obj:`True`, :math:`\epsilon`
will be a trainable parameter. (default: :obj:`False`)
**kwargs: optional
Additional arguments of
:class:`gammagl.layers.conv.MessagePassing`.
"""
def __init__(self, nn, eps=0., train_eps: bool = False,
**kwargs):
super().__init__(**kwargs)
self.nn = nn
if train_eps:
self.eps = tlx.nn.Parameter(tlx.convert_to_tensor([eps]))
else:
self.eps = tlx.convert_to_tensor([eps])
[docs]
def forward(self, x, edge_index, size=None):
if not isinstance(x, (list, tuple)):
x = (x, x)
assert len(x) == 2
out = self.propagate(x=x[0], edge_index=edge_index, size=size)
x_r = x[1]
if x_r is not None:
out += (1 + self.eps) * x_r
return self.nn(out)
[docs]
def message(self, x, edge_index):
return tlx.gather(x, edge_index[0, :])