/
graph.py
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/
graph.py
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import torch
import random
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.parameter import Parameter
from torch.nn.modules.module import Module
from collections import defaultdict
def normalize(mx):
"""Row-normalize sparse matrix"""
rowsum = torch.sum(mx, 0)
r_inv = torch.pow(rowsum, -0.5)
r_inv[torch.isinf(r_inv)] = 0.
r_mat_inv = torch.diag(r_inv)
colsum = torch.sum(mx, 1)
c_inv = torch.pow(colsum, -0.5)
c_inv[torch.isinf(c_inv)] = 0.
c_mat_inv = torch.diag(c_inv)
mx = torch.matmul(mx, r_mat_inv.int())
mx = torch.matmul(c_mat_inv.int(), mx)
return mx
class Encoder(Module):
def __init__(self, input_dim, hidden_dim, num_users, num_items, num_classes, act, dropout, bias=True):
super(Encoder, self).__init__()
self.act = act
self.dropout = nn.Dropout(dropout)
self.u_weight = Parameter(torch.randn(num_classes, input_dim, hidden_dim))
self.v_weight = Parameter(torch.randn(num_classes, input_dim, hidden_dim))
if bias:
self.u_bias = Parameter(torch.randn(hidden_dim))
self.v_bias = self.u_bias
else:
self.u_bias = None
self.v_bias = None
for w in [self.u_weight, self.v_weight]:
nn.init.xavier_normal_(w)
def forward(self, u_feat, v_feat, u, v, support):
u_feat = self.dropout(u_feat)
v_feat = self.dropout(v_feat)
supports_u = []
supports_v = []
u_weight, v_weight = 0, 0
for r in range(support.shape[0]):
u_weight = u_weight + self.u_weight[r]
v_weight = v_weight + self.v_weight[r]
tmp_u = torch.mm(u_feat, u_weight)
tmp_v = torch.mm(v_feat, v_weight)
support_norm = normalize(support[r])
support_norm_t = normalize(support[r].t())
supports_u.append(torch.mm(support_norm[u], tmp_v.int()))
supports_v.append(torch.mm(support_norm_t[v], tmp_u.int()))
z_u = torch.sum(torch.stack(supports_u, 0), 0)
z_v = torch.sum(torch.stack(supports_v, 0), 0)
if self.u_bias is not None:
z_u = z_u + self.u_bias
z_v = z_v + self.v_bias
u_outputs = self.act(z_u)
v_outputs = self.act(z_v)
return u_outputs, v_outputs
class Decoder(Module):
def __init__(self, num_users, num_items, input_dim, output_dim, init,
dropout=0.7):
super(Decoder, self).__init__()
self.dropout = nn.Dropout(dropout)
self.weight = nn.Sequential(
nn.Linear(input_dim, input_dim),
nn.LeakyReLU(),
nn.Linear(input_dim, output_dim)
)
self.init = nn.init.xavier_normal_
# self.init = select(init)
self.weight.apply(self.init_weight)
def init_weight(self, m):
if isinstance(m, nn.Linear):
self.init(m.weight)
if m.bias is not None:
self.init(m.bias)
def forward(self, u_hidden, v_hidden):
u_hidden = self.dropout(u_hidden)
v_hidden = self.dropout(v_hidden)
u_w = self.weight(u_hidden)
v_w = self.weight(v_hidden)
return u_w, v_w
class GAE(nn.Module):
def __init__(self, num_users, num_items, num_classes,
u_features, v_features,
hidden, dropout, args):
super(GAE, self).__init__()
self.num_users = num_users
self.num_items = num_items
self.dropout = dropout
self.u_features = u_features
self.v_features = v_features
input_dim = u_features.shape[1]
self.projector = nn.Sequential(
nn.Linear(input_dim, 32, bias=False),
nn.LeakyReLU(),
nn.Linear(32, hidden[0], bias=False)
)
self.gcl1 = Encoder(hidden[0], hidden[1],
num_users, num_items,
num_classes, torch.relu, self.dropout, bias=True)
self.gcl2 = Encoder(hidden[1], hidden[2],
num_users, num_items,
num_classes, torch.relu, self.dropout, bias=True)
self.denseu = nn.Linear(hidden[2], hidden[3], bias=False)
self.densev = nn.Linear(hidden[2], hidden[3], bias=False)
self.decoder = Decoder(num_users=num_users, num_items=num_items,
output_dim=input_dim,
input_dim=hidden[3],
dropout=0.5, init=args.init)
self.inf_predictor = nn.Sequential(
nn.Linear(input_dim, 64),
nn.ReLU(),
nn.Linear(64, 1)
)
self.load_influence_pretrain(args.dataName, args.recommender)
self.load_encoder_from_pretrained(args.dataName, args.recommender)
def load_influence_pretrain(self, dataset, model_name):
self.inf_predictor.load_state_dict(torch.load(f'./saved/influence/{dataset}_{model_name}.pt'))
for param in self.inf_predictor.parameters():
param.requires_grad = False
def load_encoder_from_pretrained(self, dataset, model_name):
self.gcl1.load_state_dict(torch.load(f'./saved/pretrain/{dataset}_{model_name}_1.pt'))
self.gcl2.load_state_dict(torch.load(f'./saved/pretrain/{dataset}_{model_name}_2.pt'))
def forward(self, u, v, r_matrix, mask, do_train=True):
if do_train:
u_features = self.u_features
u_features[mask] = 0.
else:
attack_size = mask.shape[0]
fake_emb = torch.zeros((attack_size, self.feat_dim))
u_features = torch.cat((self.user_feat, fake_emb), dim=0)
u_features = self.projector(self.u_features)
v_features = self.projector(self.v_features)
u_z, v_z = self.gcl1(u_features, v_features, torch.arange(self.num_users), torch.arange(self.num_items), r_matrix)
u_z, v_z = self.gcl2(u_z, v_z, u, v, r_matrix)
u_h = self.denseu(F.dropout(u_z, self.dropout))
v_h = self.densev(F.dropout(v_z, self.dropout))
recon_u, recon_v = self.decoder(u_h, v_h)
score = self.inf_predictor(recon_u[mask])
return recon_u, recon_v, score
def gumbel_top_k(logits, k, temperature=1.0, eps=1e-10):
gumbel_noise = -torch.log(-torch.log(torch.rand_like(logits) + eps) + eps)
gumbel_logits = (logits + gumbel_noise) / temperature
_, top_k_indices = torch.topk(gumbel_logits, k, dim=-1, largest=True, sorted=False)
one_hot = torch.zeros_like(logits)
one_hot.scatter_(-1, top_k_indices, 1)
return one_hot
class SubGraphGen(nn.Module):
def __init__(self, user_feat, item_feat, num_classes, trainMatrix, args):
super(SubGraphGen).__init__()
drop, pop_thr, hidden = args.drop, args.pop_thr, args.hidden
self.feat_dim, self.trainMatrix = user_feat.shape[1], trainMatrix
self.user_feat, self.item_feat = user_feat, item_feat
self.hot_items = self.popular(trainMatrix, pop_thr)
self.num_users, self.num_items = user_feat.shape[0], item_feat.shape[0]
self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
self.feat_gen = GAE(self.num_users, self.num_items, num_classes, user_feat, item_feat, hidden, drop, args)
self.projector = nn.Sequential(
nn.Linear(self.feat_dim, self.feat_dim * 2),
nn.ReLU(),
nn.Linear(self.feat_dim * 2, self.feat_dim)
)
self.activate = nn.LeakyReLU()
self.get_adj_list(trainMatrix)
def get_adj_list(self, trainMatrix):
user_adj_list, item_adj_list = defaultdict(set), defaultdict(set)
for user in range(trainMatrix.shape[0]):
items = np.nonzero(trainMatrix[user, :])[0]
user_adj_list[user].update(items)
for item in range(trainMatrix.shape[1]):
users = np.nonzero(trainMatrix[:, item])[0]
item_adj_list[item].update(users)
self.user_adj_list = user_adj_list
self.item_adj_list = item_adj_list
def popular(self, trainMatrix, pop_thr):
item_popularity = np.count_nonzero(trainMatrix, axis=0)
hot_items = np.argsort(item_popularity)[::-1][:int(pop_thr * trainMatrix.shape[1])]
return hot_items
def fusion(self, target, attack_size, u, v, r_matrix, do_train):
mask = random.sample(u, attack_size)
recon_u, recon_v, score = self.feat_gen(u, v, r_matrix, mask, do_train=do_train)
fake_user_emb = recon_u[mask]
if do_train:
user_emb = self.projector(fake_user_emb)
return user_emb, recon_u, recon_v, score
else:
sechop = set()
for neighbor in list(self.item_adj_list[target]):
neighbors_item = self.user_adj_list[neighbor]
indices = [i + self.n_user for i in neighbors_item]
sechop.update(indices)
return user_emb, sechop, recon_u, recon_v, score
def stack(self, tensor):
sub_tensors = []
for i in range(self.num_classes):
mask = (tensor == i)
sub_tensors.append(mask.int() * i)
output_tensor = (torch.stack(sub_tensors, dim=0))
return output_tensor
def forward(self, target, attack_size, budget, n_ran, do_train):
r_matrix = self.stack(self.trainMatrix)
candiate = None
if do_train:
fake_emb, recon_u, recon_v, score = self.fusion(target, attack_size, range(self.num_users), range(self.num_items), r_matrix, do_train)
else:
fake_emb, candidate, recon_u, recon_v, score = self.fusion(target, attack_size, range(self.num_users), range(self.num_items), r_matrix, do_train)
node_emb = self.projector(self.item_feat)
edges = F.cosine_similarity(fake_emb.unsqueeze(1), node_emb.unsqueeze(0), dim=-1)
edges = gumbel_top_k(edges, budget)
if not do_train:
candiate = np.tile(np.array(list(candidate)), (attack_size, 1))
random_pop = np.array([np.random.choice(self.hot_items, n_ran) for _ in range(attack_size)])
candiate = np.concatenate((candiate, random_pop), axis=1)
node_emb = self.projector(self.item_feat)[candiate]
return recon_u, recon_v, score, edges, candidate