layers_sub_Mean.py

import math
import torch
from torch.nn.parameter import Parameter
from torch.nn.modules.module import Module


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

    def __init__(self, in_features, out_features, Graph_obj, bias=True):
        super(GraphConvolution, self).__init__()
        self.in_features = in_features
        self.out_features = out_features
        self.weight = Parameter(torch.FloatTensor(in_features, out_features))
        self.G = Graph_obj
        if bias:
            self.bias = Parameter(torch.FloatTensor(out_features))
        else:
            self.register_parameter('bias', None)
        self.reset_parameters()

    def reset_parameters(self):
        stdv = 1. / math.sqrt(self.weight.size(1))
        self.weight.data.uniform_(-stdv, stdv)
        if self.bias is not None:
            self.bias.data.uniform_(-stdv, stdv)

    def forward(self, input, adj_matrix, degree_norm):
        mapped_fea = torch.mm(input, self.weight)

        aggregate_1hop_emb = torch.mm(adj_matrix, mapped_fea)
        avg_1hop_emb = aggregate_1hop_emb * degree_norm
        new_emb_mat = mapped_fea - avg_1hop_emb

        if self.bias is not None:
            return new_emb_mat + self.bias
        else:
            return new_emb_mat

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