import torch
import torch.nn as nn
from graphwar.nn.layers import GCNConv, Sequential, activations
from graphwar.defense import GNNGUARD as GNNGUARDLayer
from graphwar.utils import wrapper
[docs]class GNNGUARD(nn.Module):
r"""Graph Convolution Network (GCN) with
:class:`graphwar.defense.GNNGUARD` from the `"GNNGUARD:
Defending Graph Neural Networks against Adversarial Attacks"
<https://arxiv.org/abs/2006.08149>`_ paper (NeurIPS'20)
Parameters
----------
in_channels : int,
the input dimensions of model
out_channels : int,
the output dimensions of model
hids : list, optional
the number of hidden units for each hidden layer, by default [16]
acts : list, optional
the activation function for each hidden layer, by default ['relu']
dropout : float, optional
the dropout ratio of model, by default 0.5
bias : bool, optional
whether to use bias in the layers, by default True
bn: bool, optional
whether to use :class:`BatchNorm1d` after the convolution layer, by default False
See also
--------
:class:`graphwar.defense.GNNGUARD`
:class:`graphwar.nn.models.GCN`
Note
----
It is convenient to extend the number of layers with different or the same
hidden units (activation functions) using :meth:`graphwar.utils.wrapper`.
See Examples below:
Examples
--------
>>> # GNNGUARD with one hidden layer
>>> model = GNNGUARD(100, 10)
>>> # GNNGUARD with two hidden layers
>>> model = GNNGUARD(100, 10, hids=[32, 16], acts=['relu', 'elu'])
>>> # GNNGUARD with two hidden layers, without activation at the first layer
>>> model = GNNGUARD(100, 10, hids=[32, 16], acts=[None, 'relu'])
>>> # GNNGUARD with very deep architectures, each layer has elu as activation function
>>> model = GNNGUARD(100, 10, hids=[16]*8, acts=['elu'])
"""
@wrapper
def __init__(self,
in_channels: int,
out_channels: int,
hids: list = [16],
acts: list = ['relu'],
dropout: float = 0.5,
bn: bool = False,
normalize: bool = True,
bias: bool = True):
super().__init__()
conv = []
conv.append(GNNGUARDLayer())
for hid, act in zip(hids, acts):
conv.append(GCNConv(in_channels,
hid,
bias=bias,
normalize=normalize))
if bn:
conv.append(nn.BatchNorm1d(hid))
conv.append(activations.get(act))
conv.append(nn.Dropout(dropout))
conv.append(GNNGUARDLayer())
in_channels = hid
conv.append(GCNConv(in_channels, out_channels,
bias=bias, normalize=normalize))
self.conv = Sequential(*conv)
[docs] def reset_parameters(self):
self.conv.reset_parameters()
[docs] def forward(self, x, edge_index, edge_weight=None):
for layer in self.conv:
if isinstance(layer, GNNGUARDLayer):
edge_index, edge_weight = layer(x, edge_index)
elif isinstance(layer, GCNConv):
x = layer(x, edge_index, edge_weight)
else:
x = layer(x)
return x