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DCRN.py
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DCRN.py
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import opt
import torch
from torch import nn
from torch.nn import Linear
import torch.nn.functional as F
from torch.nn import Module, Parameter
# AE encoder from DFCN
class AE_encoder(nn.Module):
def __init__(self, ae_n_enc_1, ae_n_enc_2, ae_n_enc_3, n_input, n_z):
super(AE_encoder, self).__init__()
self.enc_1 = Linear(n_input, ae_n_enc_1)
self.enc_2 = Linear(ae_n_enc_1, ae_n_enc_2)
self.enc_3 = Linear(ae_n_enc_2, ae_n_enc_3)
self.z_layer = Linear(ae_n_enc_3, n_z)
self.act = nn.LeakyReLU(0.2, inplace=True)
def forward(self, x):
z = self.act(self.enc_1(x))
z = self.act(self.enc_2(z))
z = self.act(self.enc_3(z))
z_ae = self.z_layer(z)
return z_ae
# AE decoder from DFCN
class AE_decoder(nn.Module):
def __init__(self, ae_n_dec_1, ae_n_dec_2, ae_n_dec_3, n_input, n_z):
super(AE_decoder, self).__init__()
self.dec_1 = Linear(n_z, ae_n_dec_1)
self.dec_2 = Linear(ae_n_dec_1, ae_n_dec_2)
self.dec_3 = Linear(ae_n_dec_2, ae_n_dec_3)
self.x_bar_layer = Linear(ae_n_dec_3, n_input)
self.act = nn.LeakyReLU(0.2, inplace=True)
def forward(self, z_ae):
z = self.act(self.dec_1(z_ae))
z = self.act(self.dec_2(z))
z = self.act(self.dec_3(z))
x_hat = self.x_bar_layer(z)
return x_hat
# Auto Encoder from DFCN
class AE(nn.Module):
def __init__(self, ae_n_enc_1, ae_n_enc_2, ae_n_enc_3, ae_n_dec_1, ae_n_dec_2, ae_n_dec_3, n_input, n_z):
super(AE, self).__init__()
self.encoder = AE_encoder(
ae_n_enc_1=ae_n_enc_1,
ae_n_enc_2=ae_n_enc_2,
ae_n_enc_3=ae_n_enc_3,
n_input=n_input,
n_z=n_z)
self.decoder = AE_decoder(
ae_n_dec_1=ae_n_dec_1,
ae_n_dec_2=ae_n_dec_2,
ae_n_dec_3=ae_n_dec_3,
n_input=n_input,
n_z=n_z)
# GNNLayer from DFCN
class GNNLayer(Module):
def __init__(self, in_features, out_features):
super(GNNLayer, self).__init__()
self.in_features = in_features
self.out_features = out_features
if opt.args.name == "dblp":
self.act = nn.Tanh()
self.weight = Parameter(torch.FloatTensor(out_features, in_features))
else:
self.act = nn.Tanh()
self.weight = Parameter(torch.FloatTensor(in_features, out_features))
torch.nn.init.xavier_uniform_(self.weight)
def forward(self, features, adj, active=False):
if active:
if opt.args.name == "dblp":
support = self.act(F.linear(features, self.weight))
else:
support = self.act(torch.mm(features, self.weight))
else:
if opt.args.name == "dblp":
support = F.linear(features, self.weight)
else:
support = torch.mm(features, self.weight)
output = torch.spmm(adj, support)
az = torch.spmm(adj, output)
return output, az
# IGAE encoder from DFCN
class IGAE_encoder(nn.Module):
def __init__(self, gae_n_enc_1, gae_n_enc_2, gae_n_enc_3, n_input):
super(IGAE_encoder, self).__init__()
self.gnn_1 = GNNLayer(n_input, gae_n_enc_1)
self.gnn_2 = GNNLayer(gae_n_enc_1, gae_n_enc_2)
self.gnn_3 = GNNLayer(gae_n_enc_2, gae_n_enc_3)
self.s = nn.Sigmoid()
def forward(self, x, adj):
z_1, az_1 = self.gnn_1(x, adj, active=True)
z_2, az_2 = self.gnn_2(z_1, adj, active=True)
z_igae, az_3 = self.gnn_3(z_2, adj, active=False)
z_igae_adj = self.s(torch.mm(z_igae, z_igae.t()))
return z_igae, z_igae_adj, [az_1, az_2, az_3], [z_1, z_2, z_igae]
# IGAE decoder from DFCN
class IGAE_decoder(nn.Module):
def __init__(self, gae_n_dec_1, gae_n_dec_2, gae_n_dec_3, n_input):
super(IGAE_decoder, self).__init__()
self.gnn_4 = GNNLayer(gae_n_dec_1, gae_n_dec_2)
self.gnn_5 = GNNLayer(gae_n_dec_2, gae_n_dec_3)
self.gnn_6 = GNNLayer(gae_n_dec_3, n_input)
self.s = nn.Sigmoid()
def forward(self, z_igae, adj):
z_1, az_1 = self.gnn_4(z_igae, adj, active=True)
z_2, az_2 = self.gnn_5(z_1, adj, active=True)
z_hat, az_3 = self.gnn_6(z_2, adj, active=True)
z_hat_adj = self.s(torch.mm(z_hat, z_hat.t()))
return z_hat, z_hat_adj, [az_1, az_2, az_3], [z_1, z_2, z_hat]
# Improved Graph Auto Encoder from DFCN
class IGAE(nn.Module):
def __init__(self, gae_n_enc_1, gae_n_enc_2, gae_n_enc_3, gae_n_dec_1, gae_n_dec_2, gae_n_dec_3, n_input):
super(IGAE, self).__init__()
# IGAE encoder
self.encoder = IGAE_encoder(
gae_n_enc_1=gae_n_enc_1,
gae_n_enc_2=gae_n_enc_2,
gae_n_enc_3=gae_n_enc_3,
n_input=n_input)
# IGAE decoder
self.decoder = IGAE_decoder(
gae_n_dec_1=gae_n_dec_1,
gae_n_dec_2=gae_n_dec_2,
gae_n_dec_3=gae_n_dec_3,
n_input=n_input)
# readout function
class Readout(nn.Module):
def __init__(self, K):
super(Readout, self).__init__()
self.K = K
def forward(self, Z):
# calculate cluster-level embedding
Z_tilde = []
# step1: split the nodes into K groups
# step2: average the node embedding in each group
n_node = Z.shape[0]
step = n_node // self.K
for i in range(0, n_node, step):
if n_node - i < 2 * step:
Z_tilde.append(torch.mean(Z[i:n_node], dim=0))
break
else:
Z_tilde.append(torch.mean(Z[i:i + step], dim=0))
# the cluster-level embedding
Z_tilde = torch.cat(Z_tilde, dim=0)
return Z_tilde.view(1, -1)
# Dual Correlation Reduction Network
class DCRN(nn.Module):
def __init__(self, n_node=None):
super(DCRN, self).__init__()
# Auto Encoder
self.ae = AE(
ae_n_enc_1=opt.args.ae_n_enc_1,
ae_n_enc_2=opt.args.ae_n_enc_2,
ae_n_enc_3=opt.args.ae_n_enc_3,
ae_n_dec_1=opt.args.ae_n_dec_1,
ae_n_dec_2=opt.args.ae_n_dec_2,
ae_n_dec_3=opt.args.ae_n_dec_3,
n_input=opt.args.n_input,
n_z=opt.args.n_z)
# Improved Graph Auto Encoder From DFCN
self.gae = IGAE(
gae_n_enc_1=opt.args.gae_n_enc_1,
gae_n_enc_2=opt.args.gae_n_enc_2,
gae_n_enc_3=opt.args.gae_n_enc_3,
gae_n_dec_1=opt.args.gae_n_dec_1,
gae_n_dec_2=opt.args.gae_n_dec_2,
gae_n_dec_3=opt.args.gae_n_dec_3,
n_input=opt.args.n_input)
# fusion parameter from DFCN
self.a = Parameter(nn.init.constant_(torch.zeros(n_node, opt.args.n_z), 0.5), requires_grad=True)
self.b = Parameter(nn.init.constant_(torch.zeros(n_node, opt.args.n_z), 0.5), requires_grad=True)
self.alpha = Parameter(torch.zeros(1))
# cluster layer (clustering assignment matrix)
self.cluster_centers = Parameter(torch.Tensor(opt.args.n_clusters, opt.args.n_z), requires_grad=True)
# readout function
self.R = Readout(K=opt.args.n_clusters)
# calculate the soft assignment distribution Q
def q_distribute(self, Z, Z_ae, Z_igae):
"""
calculate the soft assignment distribution based on the embedding and the cluster centers
Args:
Z: fusion node embedding
Z_ae: node embedding encoded by AE
Z_igae: node embedding encoded by IGAE
Returns:
the soft assignment distribution Q
"""
q = 1.0 / (1.0 + torch.sum(torch.pow(Z.unsqueeze(1) - self.cluster_centers, 2), 2))
q = (q.t() / torch.sum(q, 1)).t()
q_ae = 1.0 / (1.0 + torch.sum(torch.pow(Z_ae.unsqueeze(1) - self.cluster_centers, 2), 2))
q_ae = (q_ae.t() / torch.sum(q_ae, 1)).t()
q_igae = 1.0 / (1.0 + torch.sum(torch.pow(Z_igae.unsqueeze(1) - self.cluster_centers, 2), 2))
q_igae = (q_igae.t() / torch.sum(q_igae, 1)).t()
return [q, q_ae, q_igae]
def forward(self, X_tilde1, Am, X_tilde2, Ad):
# node embedding encoded by AE
Z_ae1 = self.ae.encoder(X_tilde1)
Z_ae2 = self.ae.encoder(X_tilde2)
# node embedding encoded by IGAE
Z_igae1, A_igae1, AZ_1, Z_1 = self.gae.encoder(X_tilde1, Am)
Z_igae2, A_igae2, AZ_2, Z_2 = self.gae.encoder(X_tilde2, Ad)
# cluster-level embedding calculated by readout function
Z_tilde_ae1 = self.R(Z_ae1)
Z_tilde_ae2 = self.R(Z_ae2)
Z_tilde_igae1 = self.R(Z_igae1)
Z_tilde_igae2 = self.R(Z_igae2)
# linear combination of view 1 and view 2
Z_ae = (Z_ae1 + Z_ae2) / 2
Z_igae = (Z_igae1 + Z_igae2) / 2
# node embedding fusion from DFCN
Z_i = self.a * Z_ae + self.b * Z_igae
Z_l = torch.spmm(Am, Z_i)
S = torch.mm(Z_l, Z_l.t())
S = F.softmax(S, dim=1)
Z_g = torch.mm(S, Z_l)
Z = self.alpha * Z_g + Z_l
# AE decoding
X_hat = self.ae.decoder(Z)
# IGAE decoding
Z_hat, Z_adj_hat, AZ_de, Z_de = self.gae.decoder(Z, Am)
sim = (A_igae1 + A_igae2) / 2
A_hat = sim + Z_adj_hat
# node embedding and cluster-level embedding
Z_ae_all = [Z_ae1, Z_ae2, Z_tilde_ae1, Z_tilde_ae2]
Z_gae_all = [Z_igae1, Z_igae2, Z_tilde_igae1, Z_tilde_igae2]
# the soft assignment distribution Q
Q = self.q_distribute(Z, Z_ae, Z_igae)
# propagated embedding AZ_all and embedding Z_all
AZ_en = []
Z_en = []
for i in range(len(AZ_1)):
AZ_en.append((AZ_1[i]+AZ_2[i])/2)
Z_en.append((Z_1[i]+Z_2[i])/2)
AZ_all = [AZ_en, AZ_de]
Z_all = [Z_en, Z_de]
return X_hat, Z_hat, A_hat, sim, Z_ae_all, Z_gae_all, Q, Z, AZ_all, Z_all