Source code for graphwar.nn.models.median_gcn
import torch
import torch.nn as nn
from graphwar.nn.layers import MedianConv, Sequential, activations
from graphwar.utils import wrapper
[docs]class MedianGCN(nn.Module):
r"""Graph Convolution Network (GCN) with
median aggregation (MedianGCN)
from the `"Understanding Structural Vulnerability
in Graph Convolutional Networks"
<https://www.ijcai.org/proceedings/2021/310>`_ paper (IJCAI'21)
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
normalize : bool, optional
whether to compute symmetric normalization
coefficients on the fly, by default False
bn: bool, optional
whether to use :class:`BatchNorm1d` after the convolution layer, by default False
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
--------
>>> # MedianGCN with one hidden layer
>>> model = MedianGCN(100, 10)
>>> # MedianGCN with two hidden layers
>>> model = MedianGCN(100, 10, hids=[32, 16], acts=['relu', 'elu'])
>>> # MedianGCN with two hidden layers, without activation at the first layer
>>> model = MedianGCN(100, 10, hids=[32, 16], acts=[None, 'relu'])
>>> # MedianGCN with very deep architectures, each layer has elu as activation function
>>> model = MedianGCN(100, 10, hids=[16]*8, acts=['elu'])
See also
--------
:class:`graphwar.nn.layers.MedianConv`
"""
@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 = False,
bias: bool = True):
super().__init__()
conv = []
assert len(hids) == len(acts)
for hid, act in zip(hids, acts):
conv.append(MedianConv(in_channels,
hid,
bias=bias,
normalize=normalize))
if bn:
conv.append(nn.BatchNorm1d(hid))
conv.append(activations.get(act))
conv.append(nn.Dropout(dropout))
in_channels = hid
conv.append(MedianConv(in_channels, out_channels,
bias=bias, normalize=normalize))
self.conv = Sequential(*conv)
[docs] def forward(self, x, edge_index, edge_weight=None):
return self.conv(x, edge_index, edge_weight)