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construct_graph.py
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construct_graph.py
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import numpy as np
import torch
from torch_geometric.utils import mask_feature,add_random_edge,dropout_adj
import copy
from torch_geometric.utils import to_dense_adj, dense_to_sparse
from torch_geometric.data import Data, InMemoryDataset
def construct_noisy_graph(data,perturb_ratio,mode='raw'):
noisy_data = copy.deepcopy(data)
if(mode=='raw'):
noisy_edge_index = data.edge_index
noisy_edge_weights = data.edge_weight
noisy_x = data.x
elif(mode=='random_noise'):
# random noise: inject/remove edges
print("raw graph:",data.edge_index.shape)
noisy_edge_index,added_edges=add_random_edge(data.edge_index,force_undirected=True,p=perturb_ratio)
print("add edge:",noisy_edge_index.shape)
noisy_edge_index,removed_edges=dropout_adj(data.edge_index,data.edge_weight,force_undirected=True,p=perturb_ratio)
print("remove edge:",noisy_edge_index.shape)
noisy_edge_index = torch.cat([noisy_edge_index,added_edges],dim=1).long()
print("updated graph:",noisy_edge_index.shape)
noisy_data.edge_index = noisy_edge_index
return noisy_data
def drop_feature(x, drop_prob):
drop_mask = torch.empty(
(x.size(1), ),
dtype=torch.float32,
device=x.device).uniform_(0, 1) < drop_prob
x = x.clone()
x[:, drop_mask] = 0
return x
def drop_adj_1by1(args,edge_index, edge_weight, p,device):
# update edge_index according to edge_weight
if(edge_weight!=None):
edge_index = edge_index[:,edge_weight.nonzero().flatten().long()]
edge_weight = torch.ones([edge_index.shape[1],]).to(device)
# rs = np.random.RandomState(args.seed)
# remain_mask = rs.binomial(1,p,edge_index.shape[1])
remain_mask = np.random.binomial(1,p,edge_index.shape[1])
remain_index = remain_mask.nonzero()[0]
remain_edge_index = edge_index[:,remain_index]
remain_edge_weight = torch.ones([remain_edge_index.shape[1],]).to(device)
return remain_edge_index,remain_edge_weight
def construct_augmentation_1(args, x, edge_index, edge_weight=None):
#
# graph 1:
noisy_edge_index,added_edges=add_random_edge(edge_index,force_undirected=True,p=args.add_edge_rate_1)
noisy_edge_index,noisy_edge_weight=dropout_adj(edge_index,edge_weight,force_undirected=True,p=args.drop_edge_rate_1)
if(len(added_edges)>0):
aug_edge_index_1 = torch.cat([noisy_edge_index,added_edges],dim=1).long()
else:
aug_edge_index_1 = noisy_edge_index.long()
aug_x_1 = drop_feature(x,drop_prob=args.drop_feat_rate_1)
aug_edge_weight_1 = edge_weight
# graph 2:
noisy_edge_index,added_edges=add_random_edge(edge_index,force_undirected=True,p=args.add_edge_rate_2)
noisy_edge_index,noisy_edge_weight=dropout_adj(edge_index,edge_weight,force_undirected=True,p=args.drop_edge_rate_2)
if(len(added_edges)>0):
aug_edge_index_2 = torch.cat([noisy_edge_index,added_edges],dim=1).long()
else:
aug_edge_index_2 = noisy_edge_index.long()
aug_x_2 = drop_feature(x,drop_prob=args.drop_feat_rate_2)
aug_edge_weight_2 = edge_weight
return aug_edge_index_1,aug_x_1,aug_edge_weight_1,aug_edge_index_2,aug_x_2,aug_edge_weight_2
def construct_augmentation_1by1(args, x, edge_index, edge_weight=None):
#
# graph 1:
noisy_edge_index,added_edges=add_random_edge(edge_index,force_undirected=True,p=args.add_edge_rate_1)
noisy_edge_index,noisy_edge_weight=dropout_adj(edge_index,edge_weight,force_undirected=True,p=args.drop_edge_rate_1)
if(len(added_edges)>0):
aug_edge_index_1 = torch.cat([noisy_edge_index,added_edges],dim=1).long()
else:
aug_edge_index_1 = noisy_edge_index.long()
aug_x_1 = drop_feature(x,drop_prob=args.drop_feat_rate_1)
aug_edge_weight_1 = noisy_edge_weight
# graph 2:
noisy_edge_index,added_edges=add_random_edge(edge_index,force_undirected=True,p=args.add_edge_rate_2)
noisy_edge_index,noisy_edge_weight=dropout_adj(edge_index,edge_weight,force_undirected=True,p=args.drop_edge_rate_2)
if(len(added_edges)>0):
aug_edge_index_2 = torch.cat([noisy_edge_index,added_edges],dim=1).long()
else:
aug_edge_index_2 = noisy_edge_index.long()
aug_x_2 = drop_feature(x,drop_prob=args.drop_feat_rate_2)
aug_edge_weight_2 = noisy_edge_weight
return aug_edge_index_1,aug_x_1,aug_edge_weight_1,aug_edge_index_2,aug_x_2,aug_edge_weight_2
# aug_edge_index_1,aug_x_1,aug_edge_index_2,aug_x_2 = construct_augmentation(noisy_data)
def construct_augmentation_overall(args, x, edge_index, edge_weight=None, device=None):
# graph 1:
aug_edge_index_1,aug_edge_weight_1 = drop_adj_1by1(args,edge_index, edge_weight, 1-args.drop_edge_rate_1,device)
aug_edge_index_1 = aug_edge_index_1.long()
aug_x_1 = drop_feature(x,drop_prob=args.drop_feat_rate_1)
# graph 2:
aug_edge_index_2,aug_edge_weight_2 = drop_adj_1by1(args,edge_index, edge_weight, 1-args.drop_edge_rate_2,device)
aug_edge_index_2 = aug_edge_index_2.long()
aug_x_2 = drop_feature(x,drop_prob=args.drop_feat_rate_2)
return aug_edge_index_1,aug_x_1,aug_edge_weight_1,aug_edge_index_2,aug_x_2,aug_edge_weight_2
def single_add_random_edges(idx_target, idx_add_nodes,device):
edge_list = []
for idx_add in idx_add_nodes:
edge_list.append([idx_target,idx_add])
edge_index = torch.tensor(edge_list).to(device).transpose(1,0)
row = torch.cat([edge_index[0], edge_index[1]])
col = torch.cat([edge_index[1],edge_index[0]])
edge_index = torch.stack([row,col])
return edge_index
def generate_node_noisy(args,data,idx_target,perturbation_size,device):
noisy_data = copy.deepcopy(data)
idx_overall = torch.tensor(range(data.num_nodes)).to(device)
# find connected nodes
idx_edge_index = (data.edge_index[0] == idx_target).nonzero().flatten()
idx_connected_nodes = data.edge_index[1][idx_edge_index]
idx_nonconnected_nodes = torch.tensor(list(set(np.array(idx_overall.cpu())) - set(np.array(idx_connected_nodes.cpu())))).to(device)
# permute the non-connected nodes
rs = np.random.RandomState(args.seed)
perm = rs.permutation(idx_nonconnected_nodes.shape[0])
idx_add_nodes = perm[:perturbation_size]
add_edge_index = single_add_random_edges(idx_target,idx_add_nodes,device)
update_edge_index = torch.cat([data.edge_index,add_edge_index],dim=1)
noisy_data.edge_index = update_edge_index
return noisy_data
def generate_node_noisy_global(args,data,perturbation_ratio,device):
rs = np.random.RandomState(args.seed)
N = data.x.shape[0]
noisy_data = copy.deepcopy(data)
perturbation_size = int(data.edge_index.shape[1] * perturbation_ratio)
edge_index_to_add = rs.randint(0, N, (2, perturbation_size))
edge_index_to_add = torch.tensor(edge_index_to_add)
# to undirect
row = torch.cat([edge_index_to_add[0], edge_index_to_add[1]])
col = torch.cat([edge_index_to_add[1],edge_index_to_add[0]])
edge_index_to_add = torch.stack([row,col]).to(device)
updated_edge_index = torch.cat([data.edge_index,edge_index_to_add],dim=1)
# updated_edge_index = torch.cat([data.edge_index,edge_index_to_add],dim=1)
noisy_data.edge_index = updated_edge_index
return noisy_data
def generate_graph_noisy(args,data,perturbation_size,device,to_undirected=True):
rs = np.random.RandomState(args.seed)
if(args.dataset == 'COLLAB'):
N = data.num_nodes
else:
N = data.x.shape[0]
noisy_data = copy.deepcopy(data)
edge_index_to_add = rs.randint(0, N, (2, perturbation_size))
edge_index_to_add = torch.tensor(edge_index_to_add)
if(to_undirected):
row = torch.cat([edge_index_to_add[0], edge_index_to_add[1]])
col = torch.cat([edge_index_to_add[1],edge_index_to_add[0]])
edge_index_to_add = torch.stack([row,col])
updated_edge_index = torch.cat([data.edge_index,edge_index_to_add],dim=1)
# updated_edge_index = torch.cat([data.edge_index,edge_index_to_add],dim=1)
noisy_data.edge_index = updated_edge_index
return noisy_data
def generate_graph_noisy(args,data,perturbation_size,device,to_undirected=True):
rs = np.random.RandomState(args.seed)
if(args.dataset == 'COLLAB'):
N = data.num_nodes
else:
N = data.x.shape[0]
noisy_data = copy.deepcopy(data)
edge_index_to_add = rs.randint(0, N, (2, perturbation_size))
edge_index_to_add = torch.tensor(edge_index_to_add)
if(to_undirected):
row = torch.cat([edge_index_to_add[0], edge_index_to_add[1]])
col = torch.cat([edge_index_to_add[1],edge_index_to_add[0]])
edge_index_to_add = torch.stack([row,col])
updated_edge_index = torch.cat([data.edge_index,edge_index_to_add],dim=1)
# updated_edge_index = torch.cat([data.edge_index,edge_index_to_add],dim=1)
noisy_data.edge_index = updated_edge_index
return noisy_data
def generate_graph_noisy_global(args,data,perturbation_ratio,device,to_undirected=True):
rs = np.random.RandomState(args.seed)
if(args.dataset == 'COLLAB'):
N = data.num_nodes
else:
N = data.x.shape[0]
noisy_data = copy.deepcopy(data)
perturbation_size = int(data.edge_index.shape[1] * perturbation_ratio)
edge_index_to_add = rs.randint(0, N, (2, perturbation_size))
edge_index_to_add = torch.tensor(edge_index_to_add)
if(to_undirected):
row = torch.cat([edge_index_to_add[0], edge_index_to_add[1]])
col = torch.cat([edge_index_to_add[1],edge_index_to_add[0]])
edge_index_to_add = torch.stack([row,col])
edge_index_to_add = edge_index_to_add.to(device)
updated_edge_index = torch.cat([data.edge_index,edge_index_to_add],dim=1)
# updated_edge_index = torch.cat([data.edge_index,edge_index_to_add],dim=1)
noisy_data.edge_index = updated_edge_index
return noisy_data