layers.py

import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.modules.module import Module
from torch.nn.parameter import Parameter
import numpy as np

class GraphConvolution(Module):
    """
    Simple GCN layer, similar to https://arxiv.org/abs/1609.02907
    """

    def __init__(self, in_features, out_features, dropout=0., act=F.relu):
        super(GraphConvolution, self).__init__()
        self.in_features = in_features
        self.out_features = out_features
        self.dropout = dropout
        self.act = act
        self.weight = Parameter(torch.FloatTensor(in_features, out_features))
        self.reset_parameters()

    def reset_parameters(self):
        torch.nn.init.xavier_uniform_(self.weight)

    def forward(self, input, adj):
        input = F.dropout(input, self.dropout, self.training)
        support = torch.mm(input, self.weight)
        output = torch.spmm(adj, support)
        output = self.act(output)
        return output

    def __repr__(self):
        return self.__class__.__name__ + ' (' \
               + str(self.in_features) + ' -> ' \
               + str(self.out_features) + ')'

class SampleDecoder(Module):
    def __init__(self, act=torch.sigmoid):
        super(SampleDecoder, self).__init__()
        self.act = act

    def forward(self, zx, zy):

        sim = (zx * zy).sum(1)
        sim = self.act(sim)

        return sim