├── model ├── __pycache__ │ └── RDMSC.cpython-39.pyc └── RDMSC.py ├── utils ├── __pycache__ │ ├── dataset.cpython-39.pyc │ ├── process.cpython-39.pyc │ ├── Attention.cpython-39.pyc │ ├── earlystopping.cpython-39.pyc │ └── loadSplitData.cpython-39.pyc ├── Attention.py ├── earlystopping.py ├── process.py ├── loadSplitData.py ├── getInteractionGraph.py ├── getEgoGraph.py └── dataset.py ├── requirements ├── README.md ├── RDMSC_Run.py └── data ├── Twitter16 ├── Twitter16_Ego_Relationships.csv └── Twitter16_label_All.txt └── Twitter15 └── Twitter15_Ego_Relationships.csv /model/__pycache__/RDMSC.cpython-39.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Coder-HenryZa/RDMSC/HEAD/model/__pycache__/RDMSC.cpython-39.pyc -------------------------------------------------------------------------------- /utils/__pycache__/dataset.cpython-39.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Coder-HenryZa/RDMSC/HEAD/utils/__pycache__/dataset.cpython-39.pyc -------------------------------------------------------------------------------- /utils/__pycache__/process.cpython-39.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Coder-HenryZa/RDMSC/HEAD/utils/__pycache__/process.cpython-39.pyc -------------------------------------------------------------------------------- /utils/__pycache__/Attention.cpython-39.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Coder-HenryZa/RDMSC/HEAD/utils/__pycache__/Attention.cpython-39.pyc -------------------------------------------------------------------------------- /utils/__pycache__/earlystopping.cpython-39.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Coder-HenryZa/RDMSC/HEAD/utils/__pycache__/earlystopping.cpython-39.pyc -------------------------------------------------------------------------------- /utils/__pycache__/loadSplitData.cpython-39.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Coder-HenryZa/RDMSC/HEAD/utils/__pycache__/loadSplitData.cpython-39.pyc -------------------------------------------------------------------------------- /requirements: -------------------------------------------------------------------------------- 1 | certifi==2023.5.7 2 | charset-normalizer==3.1.0 3 | colorama==0.4.6 4 | contourpy==1.0.7 5 | cycler==0.11.0 6 | emoji==2.2.0 7 | fonttools==4.39.4 8 | idna==3.4 9 | importlib-resources==5.12.0 10 | Jinja2==3.1.2 11 | joblib==1.2.0 12 | kiwisolver==1.4.4 13 | MarkupSafe==2.1.2 14 | matplotlib==3.7.1 15 | numpy==1.24.3 16 | packaging==23.1 17 | pandas==2.0.1 18 | Pillow==9.5.0 19 | psutil==5.9.5 20 | PyMySQL==1.0.3 21 | pyparsing==3.0.9 22 | python-dateutil==2.8.2 23 | pytz==2023.3 24 | requests==2.30.0 25 | scikit-learn==1.2.2 26 | scipy==1.10.1 27 | six==1.16.0 28 | threadpoolctl==3.1.0 29 | tqdm==4.65.0 30 | typing_extensions==4.5.0 31 | tzdata==2023.3 32 | urllib3==2.0.2 33 | zipp==3.15.0 34 | -------------------------------------------------------------------------------- /utils/Attention.py: -------------------------------------------------------------------------------- 1 | import torch 2 | import torch.nn as nn 3 | import torch.nn.functional as F 4 | import math 5 | 6 | 7 | class Attention(nn.Module): 8 | def __init__(self, init_size, hidden_size, output_size): 9 | 10 | super(Attention, self).__init__() 11 | 12 | self.init_size = init_size 13 | self.hidden_size = hidden_size 14 | self.output_size = output_size 15 | 16 | self.line_q = nn.Linear(self.init_size, self.hidden_size, bias=False) 17 | self.line_k = nn.Linear(self.init_size, self.hidden_size, bias=False) 18 | self.line_v = nn.Linear(self.init_size, self.output_size, bias=False) 19 | 20 | def forward(self, query, key, value, mask=None, dropout=None): 21 | 22 | query = self.line_q(query) 23 | key = self.line_k(key) 24 | d_k = query.size(-1) 25 | scores = torch.matmul(query, key.transpose(-2, -1)) / math.sqrt(d_k) 26 | 27 | if mask is not None: 28 | scores = scores.masked_fill(mask == 0, -1e9) 29 | 30 | p_attn = F.softmax(scores, dim=-1) 31 | 32 | if dropout is not None: 33 | p_attn = F.dropout(p_attn,p=dropout,training=self.training) 34 | 35 | return self.line_v(torch.matmul(p_attn, value)) -------------------------------------------------------------------------------- /utils/earlystopping.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | import torch 3 | 4 | class EarlyStopping: 5 | """Early stops the training if validation loss doesn't improve after a given patience.""" 6 | def __init__(self, patience=7, verbose=False): 7 | """ 8 | Args: 9 | patience (int): How long to wait after last time validation loss improved. 10 | Default: 7 11 | verbose (bool): If True, prints a message for each validation loss improvement. 12 | Default: False 13 | """ 14 | self.patience = patience 15 | self.verbose = verbose 16 | self.counter = 0 17 | self.best_score = None 18 | self.early_stop = False 19 | self.accs=0 20 | self.val_loss_min = np.Inf 21 | 22 | def __call__(self, val_loss, accs,model,modelname,str): 23 | 24 | score = -val_loss 25 | 26 | if self.best_score is None: 27 | self.best_score = score 28 | self.accs = accs 29 | elif score < self.best_score: 30 | self.counter += 1 31 | # print('EarlyStopping counter: {} out of {}'.format(self.counter,self.patience)) 32 | if self.counter >= self.patience: 33 | self.early_stop = True 34 | else: 35 | self.best_score = score 36 | self.accs = accs 37 | self.counter = 0 38 | 39 | def save_checkpoint(self, val_loss, model,modelname,str): 40 | 41 | torch.save(model.state_dict(),modelname+str+'.m') 42 | self.val_loss_min = val_loss -------------------------------------------------------------------------------- /utils/process.py: -------------------------------------------------------------------------------- 1 | import os 2 | from utils.dataset import GraphDataset,UdGraphDataset,BiGraphDataset 3 | 4 | 5 | cwd, _ = os.path.split(os.getcwd()) 6 | cwd, _ = os.path.split(cwd) 7 | 8 | ################################### load tree##################################### 9 | def loadTree(dataname): 10 | treePath = './data/' + dataname + '/data.TD_RvNN.vol_5000.txt' 11 | print("reading twitter tree") 12 | treeDic = {} 13 | for line in open(treePath): 14 | line = line.rstrip() 15 | eid, indexP, indexC = line.split('\t')[0], line.split('\t')[1], int(line.split('\t')[2]) 16 | max_degree, maxL, Vec = int(line.split('\t')[3]), int(line.split('\t')[4]), line.split('\t')[5] 17 | if not treeDic.__contains__(eid): 18 | treeDic[eid] = {} 19 | treeDic[eid][indexC] = {'parent': indexP, 'max_degree': max_degree, 'maxL': maxL, 'vec': Vec} 20 | print('tree no:', len(treeDic)) 21 | 22 | return treeDic 23 | 24 | ################################# load data ################################### 25 | def loadData(dataname, treeDic,fold_x_train,fold_x_test,droprate): 26 | data_path=os.path.join(cwd, 'data', dataname+'graph') 27 | print("loading train set", ) 28 | traindata_list = GraphDataset(fold_x_train, treeDic, droprate=droprate,data_path= data_path) 29 | print("train no:", len(traindata_list)) 30 | print("loading test set", ) 31 | testdata_list = GraphDataset(fold_x_test, treeDic,data_path= data_path) 32 | print("test no:", len(testdata_list)) 33 | return traindata_list, testdata_list 34 | 35 | 36 | def loadUdData(dataname, treeDic, fold_x_train, fold_x_test, droprate): 37 | 38 | print("loading train set", ) 39 | traindata_list = UdGraphDataset(fold_x_train, treeDic, dataname=dataname) 40 | print("train no:", len(traindata_list)) 41 | print("loading test set", ) 42 | testdata_list = UdGraphDataset(fold_x_test, treeDic, dataname=dataname) 43 | print("test no:", len(testdata_list)) 44 | return traindata_list, testdata_list 45 | 46 | 47 | def loadBiData(dataname, treeDic, fold_x_train, fold_x_test, TDdroprate,BUdroprate): 48 | data_path = os.path.join(cwd,'data', dataname + 'graph') 49 | print("loading train set", ) 50 | traindata_list = BiGraphDataset(fold_x_train, treeDic, tddroprate=TDdroprate, budroprate=BUdroprate, data_path=data_path) 51 | print("train no:", len(traindata_list)) 52 | print("loading test set", ) 53 | testdata_list = BiGraphDataset(fold_x_test, treeDic, data_path=data_path) 54 | print("test no:", len(testdata_list)) 55 | return traindata_list, testdata_list -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | ## Source code for the Information Sciences paper "Rumor Detection on Social Media through Mining the Social Circles with High Homogeneity" 2 | 3 | ### Requirements 4 | 5 | Code developed and tested in Python 3.9 using PyTorch 1.10.2 and Torch-geometric 2.2.0. Please refer to their official websites for installation and setup. 6 | 7 | Some major dependencies are as follows: 8 | 9 | ``` 10 | certifi==2023.5.7 11 | charset-normalizer==3.1.0 12 | colorama==0.4.6 13 | contourpy==1.0.7 14 | cycler==0.11.0 15 | emoji==2.2.0 16 | fonttools==4.39.4 17 | idna==3.4 18 | importlib-resources==5.12.0 19 | joblib==1.2.0 20 | kiwisolver==1.4.4 21 | MarkupSafe==2.1.2 22 | matplotlib==3.7.1 23 | numpy==1.24.3 24 | packaging==23.1 25 | pandas==2.0.1 26 | Pillow==9.5.0 27 | psutil==5.9.5 28 | PyMySQL==1.0.3 29 | pyparsing==3.0.9 30 | python-dateutil==2.8.2 31 | pytz==2023.3 32 | requests==2.30.0 33 | scikit-learn==1.2.2 34 | scipy==1.10.1 35 | six==1.16.0 36 | threadpoolctl==3.1.0 37 | tqdm==4.65.0 38 | typing_extensions==4.5.0 39 | tzdata==2023.3 40 | urllib3==2.0.2 41 | zipp==3.15.0 42 | ``` 43 | 44 | ### Datasets 45 | 46 | Data of Twitter15 and Twitter16 social interaction graphs follows this paper: 47 | 48 | Tian Bian, Xi Xiao, Tingyang Xu, Peilin Zhao, Wenbing Huang, Yu Rong, Junzhou Huang. Rumor Detectionon Social Media with Bi-Directional Graph Convolutional Networks. AAAI 2020. 49 | 50 | The raw datasets can be respectively downloaded from https://www.dropbox.com/s/7ewzdrbelpmrnxu/rumdetect2017.zip?dl=0. 51 | 52 | User information was crawled by the Twitter developer tool Tweepy around February 2022, when restrictions were not particularly strict. twitter15_User_Information.csv, Twitter15_User_Friends.csv and Twitter15_Ego_ Relationships.csv represent user information, friend information, and connections between them, respectively. The meaning of each field in the tables can be found as follows: 53 | 54 | https://developer.twitter.com/en/docs/twitter-api/v1/data-dictionary/object-model/user. 55 | 56 | In order to meet Twitter's privacy protocol, we have removed some fields and limited the number of friends per user. 57 | 58 | ### Run 59 | 60 | ``` 61 | # Data pre-processing 62 | python ./utils/getInteractionGraph.py Twitter15 63 | python ./utils/getInteractionGraph.py Twitter16 64 | python ./utils/getEgoGraph.py Twitter15 65 | python ./utils/getEgoGraph.py Twitter16 66 | # run 67 | python RDMSC_Run.py 68 | ``` 69 | 70 | ### Citation 71 | 72 | If you find this repository useful, please kindly consider citing the following paper: 73 | 74 | ``` 75 | @article{ZHENG2023119083, 76 | title = {Rumor detection on social media through mining the social circles with high homogeneity}, 77 | journal = {Information Sciences}, 78 | volume = {642}, 79 | pages = {119083}, 80 | year = {2023}, 81 | issn = {0020-0255}, 82 | doi = {https://doi.org/10.1016/j.ins.2023.119083}, 83 | url = {https://www.sciencedirect.com/science/article/pii/S0020025523006680}, 84 | author = {Peng Zheng and Zhen Huang and Yong Dou and Yeqing Yan}, 85 | keywords = {Rumor detection, Social media, Social circles, Homogeneity} 86 | } 87 | ``` 88 | 89 | -------------------------------------------------------------------------------- /model/RDMSC.py: -------------------------------------------------------------------------------- 1 | import torch as th 2 | from torch_scatter import scatter_mean,scatter_max 3 | from torch_geometric.nn import GCNConv 4 | from utils.Attention import Attention 5 | import copy 6 | import torch.nn.functional as F 7 | 8 | device = th.device('cuda:0' if th.cuda.is_available() else 'cpu') 9 | 10 | class EgoEncoder(th.nn.Module): 11 | def __init__(self, in_feats, hid_feats, out_feats): 12 | super(EgoEncoder, self).__init__() 13 | self.conv1 = GCNConv(in_feats, hid_feats) 14 | self.conv2 = GCNConv(hid_feats + in_feats, out_feats) 15 | self.droupout_rate = 0.2 16 | self.w1 = th.nn.Linear(hid_feats * 3, hid_feats * 3) 17 | self.dropout = th.nn.Dropout(self.droupout_rate) 18 | 19 | def forward(self, data): 20 | x, edge_index = data.x, data.edge_index 21 | x1 = copy.copy(x.float()) 22 | x = self.conv1(x, edge_index) 23 | x2 = copy.copy(x) 24 | rootindex = data.rootindex 25 | root_extend = th.zeros(len(data.batch), x1.size(1)).to(device) 26 | batch_size = max(data.batch) + 1 27 | for num_batch in range(batch_size): 28 | index = (th.eq(data.batch, num_batch)) 29 | root_extend[index] = x1[rootindex[num_batch]] 30 | x = th.cat((x, root_extend), 1) 31 | 32 | x = F.relu(x) 33 | x = F.dropout(x, p=self.droupout_rate, training=self.training) 34 | x = self.conv2(x, edge_index) 35 | x = F.relu(x) 36 | 37 | root_extend = th.zeros(len(data.batch), x2.size(1)).to(device) 38 | for num_batch in range(batch_size): 39 | index = (th.eq(data.batch, num_batch)) 40 | root_extend[index] = x2[rootindex[num_batch]] 41 | x = th.cat((x, root_extend,x2), 1) 42 | 43 | x = scatter_mean(F.relu(self.w1(x)), data.batch, dim=0) 44 | 45 | return x 46 | 47 | 48 | class InteractionEncoder(th.nn.Module): 49 | def __init__(self, in_feats, hid_feats, out_feats): 50 | super(InteractionEncoder, self).__init__() 51 | self.conv1 = GCNConv(in_feats, hid_feats) 52 | self.conv2 = GCNConv(hid_feats + in_feats, out_feats) 53 | self.w1 = th.nn.Linear(hid_feats * 3, hid_feats * 3) 54 | 55 | def forward(self, data): 56 | 57 | x, edge_index = data.x, data.edge_index 58 | x1 = copy.copy(x.float()) 59 | x = self.conv1(x, edge_index) 60 | x2 = copy.copy(x) 61 | rootindex = data.rootindex 62 | root_extend = th.zeros(len(data.batch), x1.size(1)).to(device) 63 | batch_size = max(data.batch) + 1 64 | for num_batch in range(batch_size): 65 | index = (th.eq(data.batch, num_batch)) 66 | root_extend[index] = x1[rootindex[num_batch]] 67 | x = th.cat((x, root_extend), 1) 68 | 69 | x = F.relu(x) 70 | x = F.dropout(x, training=self.training) 71 | x = self.conv2(x, edge_index) 72 | x = F.relu(x) 73 | root_extend = th.zeros(len(data.batch), x2.size(1)).to(device) 74 | for num_batch in range(batch_size): 75 | index = (th.eq(data.batch, num_batch)) 76 | root_extend[index] = x2[rootindex[num_batch]] 77 | x = th.cat((x, root_extend, x2), 1) 78 | 79 | x = scatter_mean(F.relu(self.w1(x)), data.batch, dim=0) 80 | 81 | return x 82 | 83 | 84 | class RDMSC(th.nn.Module): 85 | def __init__(self, in_feats, hid_feats, out_feats, atten_out_dim): 86 | super(RDMSC, self).__init__() 87 | self.EgoEncoder = EgoEncoder(10, hid_feats, out_feats) 88 | self.InteractionEncoder = InteractionEncoder(in_feats, hid_feats, out_feats) 89 | self.Atten = Attention(hid_feats * 3, hid_feats * 3, atten_out_dim) 90 | self.fc = th.nn.Linear(hid_feats * 6 + atten_out_dim, 4) 91 | 92 | def forward(self, data, data2): 93 | EE_x = self.EgoEncoder(data2) 94 | IE_x = self.InteractionEncoder(data) 95 | query = copy.deepcopy(IE_x.detach()) 96 | key = value = copy.deepcopy(EE_x.detach()) 97 | Attn = self.Atten(query=query, key=key, value=value, dropout=0.5) 98 | x = th.cat((IE_x, EE_x, Attn), 1) 99 | x = F.dropout(x, p=0.2, training=self.training) 100 | x = F.relu(x) 101 | x = self.fc(x) 102 | x = F.log_softmax(x, dim=1) 103 | return x -------------------------------------------------------------------------------- /utils/loadSplitData.py: -------------------------------------------------------------------------------- 1 | import random 2 | from random import shuffle 3 | import os 4 | 5 | # cwd1=os.getcwd() 6 | # cwd1,_=os.path.split(os.getcwd()) 7 | 8 | def loadSplitData(obj, fold = 3): 9 | 10 | labelPath = './data/' + obj + '/' + obj + '_label_All.txt' 11 | labelset_nonR, labelset_f, labelset_t, labelset_u = ['news', 'non-rumor'], ['false'], ['true'], ['unverified'] 12 | print("loading tree label" ) 13 | NR,F,T,U = [],[],[],[] 14 | l1=l2=l3=l4=0 15 | labelDic = {} 16 | for line in open(labelPath): 17 | line = line.rstrip() 18 | label, eid = line.split('\t')[0], line.split('\t')[2] 19 | labelDic[eid] = label.lower() 20 | if label in labelset_nonR: 21 | NR.append(eid) 22 | l1 += 1 23 | if labelDic[eid] in labelset_f: 24 | F.append(eid) 25 | l2 += 1 26 | if labelDic[eid] in labelset_t: 27 | T.append(eid) 28 | l3 += 1 29 | if labelDic[eid] in labelset_u: 30 | U.append(eid) 31 | l4 += 1 32 | print(len(labelDic)) 33 | print(l1,l2,l3,l4) 34 | 35 | fold0_x_test,fold1_x_test,fold2_x_test,fold3_x_test,fold4_x_test=[],[],[],[],[] 36 | fold0_x_train, fold1_x_train, fold2_x_train, fold3_x_train, fold4_x_train = [], [], [], [], [] 37 | leng1 = int(l1 * 0.2) 38 | leng2 = int(l2 * 0.2) 39 | leng3 = int(l3 * 0.2) 40 | leng4 = int(l4 * 0.2) 41 | # 第0个测试集 42 | fold0_x_test.extend(NR[0:leng1]) 43 | fold0_x_test.extend(F[0:leng2]) 44 | fold0_x_test.extend(T[0:leng3]) 45 | fold0_x_test.extend(U[0:leng4]) 46 | fold0_x_train.extend(NR[leng1:]) 47 | fold0_x_train.extend(F[leng2:]) 48 | fold0_x_train.extend(T[leng3:]) 49 | fold0_x_train.extend(U[leng4:]) 50 | # 第1个测试集 51 | fold1_x_test.extend(NR[leng1:leng1*2]) 52 | fold1_x_test.extend(F[leng2:leng2*2]) 53 | fold1_x_test.extend(T[leng3:leng3*2]) 54 | fold1_x_test.extend(U[leng4:leng4*2]) 55 | fold1_x_train.extend(NR[0:leng1]) 56 | fold1_x_train.extend(NR[leng1 * 2:]) 57 | fold1_x_train.extend(F[0:leng2]) 58 | fold1_x_train.extend(F[leng2 * 2:]) 59 | fold1_x_train.extend(T[0:leng3]) 60 | fold1_x_train.extend(T[leng3 * 2:]) 61 | fold1_x_train.extend(U[0:leng4]) 62 | fold1_x_train.extend(U[leng4 * 2:]) 63 | 64 | # 第二个数据集 65 | fold2_x_train.extend(NR[0:leng1*2]) 66 | fold2_x_train.extend(NR[leng1*3:]) 67 | fold2_x_train.extend(F[0:leng2*2]) 68 | fold2_x_train.extend(F[leng2*3:]) 69 | fold2_x_train.extend(T[0:leng3*2]) 70 | fold2_x_train.extend(T[leng3*3:]) 71 | fold2_x_train.extend(U[0:leng4*2]) 72 | fold2_x_train.extend(U[leng4*3:]) 73 | fold2_x_test.extend(NR[leng1*2:leng1*3]) 74 | fold2_x_test.extend(F[leng2*2:leng2*3]) 75 | fold2_x_test.extend(T[leng3*2:leng3*3]) 76 | fold2_x_test.extend(U[leng4*2:leng4*3]) 77 | 78 | #第三个数据集 79 | fold3_x_train.extend(NR[0:leng1*3]) 80 | fold3_x_train.extend(NR[leng1*4:]) 81 | fold3_x_train.extend(F[0:leng2*3]) 82 | fold3_x_train.extend(F[leng2*4:]) 83 | fold3_x_train.extend(T[0:leng3*3]) 84 | fold3_x_train.extend(T[leng3*4:]) 85 | fold3_x_train.extend(U[0:leng4*3]) 86 | fold3_x_train.extend(U[leng4*4:]) 87 | fold3_x_test.extend(NR[leng1*3:leng1*4]) 88 | fold3_x_test.extend(F[leng2*3:leng2*4]) 89 | fold3_x_test.extend(T[leng3*3:leng3*4]) 90 | fold3_x_test.extend(U[leng4*3:leng4*4]) 91 | 92 | # 第四个数据集 93 | fold4_x_train.extend(NR[0:leng1*4]) 94 | fold4_x_train.extend(NR[leng1*5:]) 95 | fold4_x_train.extend(F[0:leng2*4]) 96 | fold4_x_train.extend(F[leng2*5:]) 97 | fold4_x_train.extend(T[0:leng3*4]) 98 | fold4_x_train.extend(T[leng3*5:]) 99 | fold4_x_train.extend(U[0:leng4*4]) 100 | fold4_x_train.extend(U[leng4*5:]) 101 | fold4_x_test.extend(NR[leng1*4:leng1*5]) 102 | fold4_x_test.extend(F[leng2*4:leng2*5]) 103 | fold4_x_test.extend(T[leng3*4:leng3*5]) 104 | fold4_x_test.extend(U[leng4*4:leng4*5]) 105 | 106 | fold0_test = list(fold0_x_test) 107 | shuffle(fold0_test) 108 | fold0_train = list(fold0_x_train) 109 | shuffle(fold0_train) 110 | fold1_test = list(fold1_x_test) 111 | shuffle(fold1_test) 112 | fold1_train = list(fold1_x_train) 113 | shuffle(fold1_train) 114 | fold2_test = list(fold2_x_test) 115 | shuffle(fold2_test) 116 | fold2_train = list(fold2_x_train) 117 | shuffle(fold2_train) 118 | fold3_test = list(fold3_x_test) 119 | shuffle(fold3_test) 120 | fold3_train = list(fold3_x_train) 121 | shuffle(fold3_train) 122 | fold4_test = list(fold4_x_test) 123 | shuffle(fold4_test) 124 | fold4_train = list(fold4_x_train) 125 | shuffle(fold4_train) 126 | 127 | data_split = [[fold0_test,fold0_train],[fold1_test,fold1_train],[fold2_test, fold2_train],[fold3_test,fold3_train],[fold4_test,fold4_train]] 128 | return data_split[fold] 129 | 130 | -------------------------------------------------------------------------------- /utils/getInteractionGraph.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | import os 3 | import numpy as np 4 | from joblib import Parallel, delayed 5 | from tqdm import tqdm 6 | import sys 7 | cwd,_=os.path.split(os.getcwd()) 8 | class Node_tweet(object): 9 | def __init__(self, idx=None): 10 | self.children = [] 11 | self.idx = idx 12 | self.word = [] 13 | self.index = [] 14 | self.parent = None 15 | 16 | def str2matrix(Str): # str = index:wordfreq index:wordfreq 17 | wordFreq, wordIndex = [], [] 18 | for pair in Str.split(' '): 19 | freq=float(pair.split(':')[1]) 20 | index=int(pair.split(':')[0]) 21 | if index<=5000: 22 | wordFreq.append(freq) 23 | wordIndex.append(index) 24 | return wordFreq, wordIndex 25 | 26 | def constructMat(tree): 27 | index2node = {} 28 | for i in tree: 29 | node = Node_tweet(idx=i) 30 | index2node[i] = node 31 | for j in tree: 32 | indexC = j 33 | indexP = tree[j]['parent'] 34 | nodeC = index2node[indexC] 35 | wordFreq, wordIndex = str2matrix(tree[j]['vec']) 36 | nodeC.index = wordIndex 37 | nodeC.word = wordFreq 38 | ## not root node ## 39 | if not indexP == 'None': 40 | nodeP = index2node[int(indexP)] 41 | nodeC.parent = nodeP 42 | nodeP.children.append(nodeC) 43 | ## root node ## 44 | else: 45 | rootindex=indexC-1 46 | root_index=nodeC.index 47 | root_word=nodeC.word 48 | rootfeat = np.zeros([1, 5000]) 49 | if len(root_index)>0: 50 | rootfeat[0, np.array(root_index)] = np.array(root_word) 51 | matrix=np.zeros([len(index2node),len(index2node)]) 52 | row=[] 53 | col=[] 54 | x_word=[] 55 | x_index=[] 56 | for index_i in range(len(index2node)): 57 | for index_j in range(len(index2node)): 58 | if index2node[index_i+1].children != None and index2node[index_j+1] in index2node[index_i+1].children: 59 | matrix[index_i][index_j]=1 60 | row.append(index_i) 61 | col.append(index_j) 62 | x_word.append(index2node[index_i+1].word) 63 | x_index.append(index2node[index_i+1].index) 64 | edgematrix=[row,col] 65 | return x_word, x_index, edgematrix,rootfeat,rootindex 66 | 67 | def getfeature(x_word,x_index): 68 | x = np.zeros([len(x_index), 5000]) 69 | for i in range(len(x_index)): 70 | if len(x_index[i])>0: 71 | x[i, np.array(x_index[i])] = np.array(x_word[i]) 72 | return x 73 | 74 | def main(obj): 75 | treePath = '../data/' + obj + '/data.TD_RvNN.vol_5000.txt' 76 | print("reading twitter tree") 77 | treeDic = {} 78 | for line in open(treePath): 79 | line = line.rstrip() 80 | eid, indexP, indexC = line.split('\t')[0], line.split('\t')[1], int(line.split('\t')[2]) 81 | max_degree, maxL, Vec = int(line.split('\t')[3]), int(line.split('\t')[4]), line.split('\t')[5] 82 | 83 | if not treeDic.__contains__(eid): 84 | treeDic[eid] = {} 85 | treeDic[eid][indexC] = {'parent': indexP, 'max_degree': max_degree, 'maxL': maxL, 'vec': Vec} 86 | print('tree no:', len(treeDic)) 87 | 88 | labelPath = '../data/' + obj + '/' + obj + '_label_All.txt' 89 | labelset_nonR, labelset_f, labelset_t, labelset_u = ['news', 'non-rumor'], ['false'], ['true'], ['unverified'] 90 | 91 | print("loading tree label") 92 | event, y = [], [] 93 | l1 = l2 = l3 = l4 = 0 94 | labelDic = {} 95 | for line in open(labelPath): 96 | line = line.rstrip() 97 | label, eid = line.split('\t')[0], line.split('\t')[2] 98 | label=label.lower() 99 | event.append(eid) 100 | if label in labelset_nonR: 101 | labelDic[eid]=0 102 | l1 += 1 103 | if label in labelset_f: 104 | labelDic[eid]=1 105 | l2 += 1 106 | if label in labelset_t: 107 | labelDic[eid]=2 108 | l3 += 1 109 | if label in labelset_u: 110 | labelDic[eid]=3 111 | l4 += 1 112 | print(len(labelDic)) 113 | print(l1, l2, l3, l4) 114 | 115 | def loadEid(event,id,y): 116 | if event is None: 117 | return None 118 | if len(event) < 2: 119 | return None 120 | if len(event)>1: 121 | x_word, x_index, tree, rootfeat, rootindex = constructMat(event) 122 | x_x = getfeature(x_word, x_index) 123 | rootfeat, tree, x_x, rootindex, y = np.array(rootfeat), np.array(tree), np.array(x_x), np.array( 124 | rootindex), np.array(y) 125 | np.savez('../data/'+obj+'_Interaction/'+id+'.npz', x=x_x,root=rootfeat,edgeindex=tree,rootindex=rootindex,y=y) 126 | return None 127 | print("loading dataset", ) 128 | Parallel(n_jobs=1, backend='threading')(delayed(loadEid)(treeDic[eid] if eid in treeDic else None,eid,labelDic[eid]) for eid in tqdm(event)) 129 | return 130 | 131 | if __name__ == '__main__': 132 | obj = sys.argv[1] 133 | main(obj) -------------------------------------------------------------------------------- /RDMSC_Run.py: -------------------------------------------------------------------------------- 1 | import sys, os 2 | 3 | sys.path.append(os.getcwd()) 4 | from utils.process import * 5 | import torch as th 6 | import torch.nn.functional as F 7 | import numpy as np 8 | from utils.earlystopping import EarlyStopping 9 | from torch_geometric.data import DataLoader 10 | from tqdm import tqdm 11 | from utils.loadSplitData import * 12 | 13 | import matplotlib.pyplot as plt 14 | from datetime import datetime 15 | from model.RDMSC import RDMSC 16 | import argparse 17 | import warnings 18 | 19 | warnings.filterwarnings("ignore") 20 | 21 | 22 | def run_model(treeDic, x_test, x_train, droprate, lr, weight_decay, patience, n_epochs, batchsize, in_feats, hid_feats, 23 | out_feats, atten_out_dim, 24 | dataname): 25 | model = RDMSC(in_feats, hid_feats, out_feats, atten_out_dim).to(device) 26 | optimizer = th.optim.Adam(model.parameters(), lr=lr, weight_decay=weight_decay) 27 | model.train() 28 | train_losses = [] 29 | val_losses = [] 30 | train_accs = [] 31 | val_accs = [] 32 | 33 | early_stopping = EarlyStopping(patience=patience, verbose=True) 34 | traindata_list, testdata_list = loadUdData(dataname, treeDic, x_train, x_test, droprate) 35 | for epoch in range(n_epochs): 36 | 37 | train_loader = DataLoader(traindata_list, batch_size=batchsize, shuffle=True, num_workers=1) 38 | test_loader = DataLoader(testdata_list, batch_size=batchsize, shuffle=True, num_workers=1) 39 | avg_loss = [] 40 | avg_acc = [] 41 | batch_idx = 0 42 | tqdm_train_loader = tqdm(train_loader) 43 | for Batch_data, Batch_data2 in tqdm_train_loader: 44 | Batch_data.to(device) 45 | Batch_data2.to(device) 46 | out_labels = model(Batch_data, Batch_data2) 47 | finalloss = F.nll_loss(out_labels, Batch_data.y) 48 | loss = finalloss 49 | optimizer.zero_grad() 50 | loss.backward() 51 | avg_loss.append(loss.item()) 52 | optimizer.step() 53 | _, pred = out_labels.max(dim=-1) 54 | correct = pred.eq(Batch_data.y).sum().item() 55 | train_acc = correct / len(Batch_data.y) 56 | avg_acc.append(train_acc) 57 | print("Epoch {:05d} | Batch{:02d} | Train_Loss {:.4f}| Train_Accuracy {:.4f}".format(epoch, batch_idx, 58 | loss.item(), 59 | train_acc)) 60 | batch_idx = batch_idx + 1 61 | 62 | train_losses.append(np.mean(avg_loss)) 63 | train_accs.append(np.mean(avg_acc)) 64 | 65 | temp_val_losses = [] 66 | temp_val_accs = [] 67 | 68 | model.eval() 69 | tqdm_test_loader = tqdm(test_loader) 70 | for Batch_data, Batch_data2 in tqdm_test_loader: 71 | Batch_data.to(device) 72 | Batch_data2.to(device) 73 | val_out = model(Batch_data, Batch_data2) 74 | val_loss = F.nll_loss(val_out, Batch_data.y) 75 | temp_val_losses.append(val_loss.item()) 76 | _, val_pred = val_out.max(dim=1) 77 | correct = val_pred.eq(Batch_data.y).sum().item() 78 | val_acc = correct / len(Batch_data.y) 79 | temp_val_accs.append(val_acc) 80 | 81 | val_losses.append(np.mean(temp_val_losses)) 82 | val_accs.append(np.mean(temp_val_accs)) 83 | 84 | print("Epoch {:05d} | Val_Loss {:.4f}| Val_Accuracy {:.4f}".format(epoch, np.mean(temp_val_losses), 85 | np.mean(temp_val_accs))) 86 | 87 | early_stopping(np.mean(temp_val_losses), np.mean(temp_val_accs), model, 'RDMSC', dataname) 88 | if early_stopping.early_stop: 89 | print("Early stopping") 90 | break 91 | 92 | show_val = list(val_accs) 93 | 94 | dt = datetime.now() 95 | save_time = dt.strftime('%Y_%m_%d_%H_%M_%S') 96 | 97 | fig = plt.figure() 98 | plt.plot(range(1, len(train_accs) + 1), train_accs, color='b', label='train') 99 | plt.plot(range(1, len(show_val) + 1), show_val, color='r', label='dev') 100 | plt.grid() 101 | plt.ylabel('accuracy') 102 | plt.xlabel('epochs') 103 | plt.legend() 104 | plt.xticks(np.arange(1, len(train_accs), step=4)) 105 | fig.savefig('result/' + '{}_accuracy_{}.png'.format(dataname, save_time)) 106 | 107 | fig = plt.figure() 108 | plt.plot(range(1, len(train_losses) + 1), train_losses, color='b', label='train') 109 | plt.plot(range(1, len(val_losses) + 1), val_losses, color='r', label='dev') 110 | plt.grid() 111 | plt.ylabel('loss') 112 | plt.xlabel('epochs') 113 | plt.legend() 114 | plt.xticks(np.arange(1, len(train_losses) + 1, step=4)) 115 | fig.savefig('result/' + '{}_loss_{}.png'.format(dataname, save_time)) 116 | 117 | return train_losses, val_losses, train_accs, val_accs 118 | 119 | 120 | def set_seed(seed=1): 121 | random.seed(seed) 122 | np.random.seed(seed) 123 | th.manual_seed(seed) 124 | th.cuda.manual_seed(seed) 125 | th.backends.cudnn.deterministic = True 126 | print("seed:", seed) 127 | 128 | 129 | parser = argparse.ArgumentParser(description='RDMSC') 130 | parser.add_argument('--lr', default=0.0005, type=float, help='Learning Rate') 131 | parser.add_argument('--weight_decay', default=1e-4, type=float, help='Weight decay coefficient') 132 | parser.add_argument('--patience', default=10, type=int, help='Early Stopping') 133 | parser.add_argument('--n_epochs', default=200, type=int, help='Training Epochs') 134 | parser.add_argument('--batchsize', default=128, type=int, help='Batch Size') 135 | parser.add_argument('--droprate', default=0.2, type=float, help='Randomly invalidate some edges') 136 | parser.add_argument('--seed', default=11, type=int) 137 | parser.add_argument('--in_feats', default=5000, type=int) 138 | parser.add_argument('--hid_feats', default=64, type=int) 139 | parser.add_argument('--out_feats', default=64, type=int) 140 | parser.add_argument('--atten_out_dim', default=4, type=int) 141 | 142 | args = parser.parse_args() 143 | 144 | if __name__ == '__main__': 145 | set_seed(args.seed) 146 | datasetname = "Twitter15" # Twitter15 Twitter16 147 | device = th.device('cuda:0' if th.cuda.is_available() else 'cpu') 148 | test_set, train_set = loadSplitData(datasetname) 149 | treeDic = loadTree(datasetname) 150 | train_losses, val_losses, train_accs, val_accs = run_model(treeDic, test_set, train_set, args.droprate, args.lr, 151 | args.weight_decay, args.patience, args.n_epochs, 152 | args.batchsize, args.in_feats, args.hid_feats, 153 | args.out_feats, args.atten_out_dim, datasetname) 154 | print("Total_Best_Accuracy:{}".format(max(val_accs))) 155 | -------------------------------------------------------------------------------- /utils/getEgoGraph.py: -------------------------------------------------------------------------------- 1 | import sys 2 | import pandas 3 | import numpy as np 4 | import datetime 5 | from dateutil import rrule 6 | from tqdm import tqdm 7 | 8 | def get_edge_index(length): 9 | a = [] 10 | b = [] 11 | for i in range(length): 12 | if i == 0: 13 | continue 14 | a.append(0) 15 | b.append(i) 16 | a.append(i) 17 | b.append(0) 18 | c = [] 19 | c.append(a) 20 | c.append(b) 21 | return c 22 | 23 | 24 | def get_monthes(create_at): 25 | # a = "2015-09-26" 26 | create_at = create_at.split(' ')[0] 27 | date_i = datetime.datetime.strptime(create_at, '%Y-%m-%d').date() 28 | # today = datetime.datetime.today().date() 29 | if obj == 'Twitter15': 30 | today = datetime.date(2022,1,11) 31 | else: 32 | today = datetime.date(2022,4,9) 33 | 34 | month_sep = rrule.rrule(rrule.MONTHLY, dtstart=date_i, until=today) 35 | return month_sep.count() 36 | 37 | def main(obj): 38 | 39 | data_1 = pandas.read_csv('../data/'+obj + '/' + obj + '_User_Information.csv',sep='\t') 40 | data_2 = pandas.read_csv('../data/'+obj + '/' + obj + '_User_Friends.csv',sep='\t') 41 | data_3 = pandas.read_csv('../data/'+obj + '/' + obj + '_Ego_Relationships.csv',sep='\t') 42 | 43 | total_feature = [] 44 | user_list = data_1['user_id'].unique().tolist() 45 | 46 | 47 | 48 | 49 | for user_id in user_list: 50 | twitter_result = data_1[data_1['user_id'] == user_id].iloc[0, :] 51 | if twitter_result['user_status'] == 0: 52 | root_feature = [] 53 | root_feature.append(user_id) 54 | for i in range(9): 55 | root_feature.append(-1) 56 | root_feature.append(twitter_result['user_status']) 57 | else: 58 | root_feature = [user_id, twitter_result['url'], twitter_result['protected'], twitter_result['verified'], 59 | twitter_result['followers_count'], twitter_result['friends_count'], 60 | twitter_result['listed_count'], twitter_result['favourites_count'], 61 | twitter_result['statuses_count'], get_monthes(twitter_result['created_at']), 62 | twitter_result['user_status']] 63 | total_feature.append(root_feature) 64 | 65 | friends_list = data_2[data_2['reason'].isna()]['friend_id'].unique().tolist() 66 | 67 | for friend_id in friends_list: 68 | twitter_result = data_2[data_2['friend_id'] == friend_id].iloc[0, :] 69 | if twitter_result['user_status'] == 0: 70 | root_feature = [] 71 | root_feature.append(friend_id) 72 | for i in range(9): 73 | root_feature.append(-1) 74 | root_feature.append(twitter_result['user_status']) 75 | else: 76 | root_feature = [friend_id, twitter_result['url'], twitter_result['protected'], twitter_result['verified'], 77 | twitter_result['followers_count'], twitter_result['friends_count'], 78 | twitter_result['listed_count'], twitter_result['favourites_count'], 79 | twitter_result['statuses_count'], get_monthes(twitter_result['created_at']), 80 | twitter_result['user_status']] 81 | total_feature.append(root_feature) 82 | 83 | data_total = pandas.DataFrame(total_feature, 84 | columns=['user_id', 'url', 'protected', 'verified', 'followers_count', 'friends_count', 85 | 'listed_count', 86 | 'favourites_count', 'statuses_count', 87 | 'created_at', 'user_status']) 88 | cols = ['url', 'protected', 'verified', 'followers_count', 'friends_count', 'listed_count', 'favourites_count', 89 | 'statuses_count', 'created_at', 'user_status'] # 可以改成自己需要的列的名字 90 | for item in cols: 91 | mean_tmp = np.mean(np.array(data_total[item])) 92 | std_tmp = np.std(np.array(data_total[item])) 93 | if (std_tmp): 94 | data_total[item] = data_total[item].apply(lambda x: (x - mean_tmp) / std_tmp) 95 | 96 | 97 | user_id_friend = data_3['user_id'].unique().tolist() 98 | 99 | standard_list = [] 100 | for index, row in data_1.iterrows(): 101 | twitter_id = row['twitter_id'] 102 | user_id = row['user_id'] 103 | tree_feature = [] 104 | # 构建root feature 105 | twitter_result = data_total[data_total['user_id'] == user_id].iloc[0, :] 106 | root_feature = [float(twitter_result['url']), float(twitter_result['protected']), float(twitter_result['verified']), 107 | float(twitter_result['followers_count']), float(twitter_result['friends_count']), 108 | float(twitter_result['listed_count']), 109 | float(twitter_result['favourites_count']), 110 | float(twitter_result['statuses_count']), float(twitter_result['created_at']), 111 | float(twitter_result['user_status'])] 112 | 113 | tree_feature.append(root_feature) 114 | 115 | if user_id in user_id_friend: 116 | friend_ids = data_2[(data_2['user_id'] == user_id) & (data_2['user_status'] == 1)]['friend_id'].tolist() 117 | for friend_id in friend_ids: 118 | twitter_result = data_total[data_total['user_id'] == friend_id].iloc[0, :] 119 | temp_feature = [float(twitter_result['url']), float(twitter_result['protected']), 120 | float(twitter_result['verified']), 121 | float(twitter_result['followers_count']), float(twitter_result['friends_count']), 122 | float(twitter_result['listed_count']), 123 | float(twitter_result['favourites_count']), 124 | float(twitter_result['statuses_count']), float(twitter_result['created_at']), 125 | float(twitter_result['user_status'])] 126 | tree_feature.append(temp_feature) 127 | temp_dict = {} 128 | temp_dict['twitter_id'] = twitter_id 129 | temp_dict['user_id'] = str(user_id) 130 | temp_dict['root_feature'] = str(root_feature) 131 | temp_dict['tree_feature'] = str(tree_feature) 132 | temp_dict['edge_index'] = str(get_edge_index(len(tree_feature))) 133 | temp_dict['root_index'] = 0 134 | temp_dict['tree_len'] = len(tree_feature) 135 | temp_dict['status'] = True 136 | standard_list.append(temp_dict) 137 | 138 | else: 139 | tree_feature.append(root_feature) 140 | temp_dict = {} 141 | temp_dict['twitter_id'] = twitter_id 142 | temp_dict['user_id'] = str(user_id) 143 | temp_dict['root_feature'] = str(root_feature) 144 | temp_dict['tree_feature'] = str(tree_feature) 145 | temp_dict['edge_index'] = str(get_edge_index(len(tree_feature))) 146 | temp_dict['root_index'] = 0 147 | temp_dict['tree_len'] = len(tree_feature) 148 | temp_dict['status'] = False 149 | standard_list.append(temp_dict) 150 | print(standard_list[:10]) 151 | 152 | print('a') 153 | for i, dic_sample in tqdm(enumerate(standard_list), total=len(standard_list), desc='Saving .npz files'): 154 | np.savez('../data/' + obj + '_Ego/'+str(dic_sample['twitter_id'])+'.npz', user_id=dic_sample['user_id'], 155 | root_feature=dic_sample['root_feature'], tree_feature=dic_sample['tree_feature'], 156 | edge_index=dic_sample['edge_index'], root_index=dic_sample['root_index'], 157 | tree_len=dic_sample['tree_len'], status=dic_sample['status']) 158 | 159 | if __name__ == '__main__': 160 | obj = sys.argv[1] 161 | main(obj) -------------------------------------------------------------------------------- /utils/dataset.py: -------------------------------------------------------------------------------- 1 | import os 2 | import numpy as np 3 | import torch 4 | import random 5 | from torch.utils.data import Dataset 6 | from torch_geometric.data import Data 7 | import pickle 8 | import pymysql 9 | import numpy as np 10 | 11 | 12 | address = {'host': 'localhost', 'user': 'root', 'passwd': 'root', 'database': 'collect_data'} 13 | db = pymysql.connect(host=address.get('host'), user=address.get('user'), passwd=address.get('passwd'), 14 | database=address.get('database')) 15 | cursor = db.cursor() 16 | 17 | np.set_printoptions(threshold=np.inf) 18 | class GraphDataset(Dataset): 19 | def __init__(self, fold_x, treeDic, lower=2, upper=100000, droprate=0, 20 | data_path=os.path.join('..', '..', 'data', 'Weibograph')): 21 | self.fold_x = list( 22 | filter(lambda id: id in treeDic and len(treeDic[id]) >= lower and len(treeDic[id]) <= upper, fold_x)) 23 | self.treeDic = treeDic 24 | self.data_path = data_path 25 | self.droprate = droprate 26 | 27 | def __len__(self): 28 | return len(self.fold_x) 29 | 30 | def __getitem__(self, index): 31 | id = self.fold_x[index] 32 | data = np.load(os.path.join(self.data_path, id + ".npz"), allow_pickle=True) 33 | edgeindex = data['edgeindex'] 34 | if self.droprate > 0: 35 | row = list(edgeindex[0]) 36 | col = list(edgeindex[1]) 37 | length = len(row) 38 | poslist = random.sample(range(length), int(length * (1 - self.droprate))) 39 | poslist = sorted(poslist) 40 | row = list(np.array(row)[poslist]) 41 | col = list(np.array(col)[poslist]) 42 | new_edgeindex = [row, col] 43 | else: 44 | new_edgeindex = edgeindex 45 | return Data(x=torch.tensor(data['x'], dtype=torch.float32), 46 | edge_index=torch.LongTensor(new_edgeindex), 47 | y=torch.LongTensor([int(data['y'])]), root=torch.LongTensor(data['root']), 48 | rootindex=torch.LongTensor([int(data['rootindex'])])) 49 | 50 | 51 | def collate_fn(data): 52 | return data 53 | 54 | 55 | class BiGraphDataset(Dataset): 56 | def __init__(self, fold_x, treeDic,lower=2, upper=100000, tddroprate=0,budroprate=0, 57 | data_path=os.path.join('..','..', 'data', 'Weibograph')): 58 | self.fold_x = list(filter(lambda id: id in treeDic and len(treeDic[id]) >= lower and len(treeDic[id]) <= upper, fold_x)) 59 | self.treeDic = treeDic 60 | self.data_path = data_path 61 | self.tddroprate = tddroprate 62 | self.budroprate = budroprate 63 | 64 | def __len__(self): 65 | return len(self.fold_x) 66 | 67 | def __getitem__(self, index): 68 | # id 为 eid，index：0,1,2,3,4,5 69 | id =self.fold_x[index] 70 | # 读取存储的npz文件 71 | data=np.load(os.path.join(self.data_path, id + ".npz"), allow_pickle=True) 72 | edgeindex = data['edgeindex'] 73 | # 正向的边矩阵，取边的数量为edge_num*(1-droprate) 74 | if self.tddroprate > 0: 75 | row = list(edgeindex[0]) 76 | col = list(edgeindex[1]) 77 | length = len(row) 78 | poslist = random.sample(range(length), int(length * (1 - self.tddroprate))) 79 | poslist = sorted(poslist) 80 | row = list(np.array(row)[poslist]) 81 | col = list(np.array(col)[poslist]) 82 | new_edgeindex = [row, col] 83 | else: 84 | new_edgeindex = edgeindex 85 | # 反向的边矩阵，为源矩阵的转置，正向的边矩阵，取边的数量为edge_num*(1-droprate) 86 | burow = list(edgeindex[1]) 87 | bucol = list(edgeindex[0]) 88 | if self.budroprate > 0: 89 | length = len(burow) 90 | poslist = random.sample(range(length), int(length * (1 - self.budroprate))) 91 | poslist = sorted(poslist) 92 | row = list(np.array(burow)[poslist]) 93 | col = list(np.array(bucol)[poslist]) 94 | bunew_edgeindex = [row, col] 95 | else: 96 | bunew_edgeindex = [burow,bucol] 97 | # new_edgeindex 正向的边矩阵，bunew_edgeindex，反向的边矩阵 98 | return Data(x=torch.tensor(data['x'],dtype=torch.float32), 99 | edge_index=torch.LongTensor(new_edgeindex),BU_edge_index=torch.LongTensor(bunew_edgeindex), 100 | y=torch.LongTensor([int(data['y'])]), root=torch.LongTensor(data['root']), 101 | rootindex=torch.LongTensor([int(data['rootindex'])])) 102 | 103 | 104 | 105 | 106 | class UdGraphDataset(Dataset): 107 | def __init__(self, fold_x, treeDic, dataname, lower=2, upper=100000, droprate=0): 108 | self.fold_x = list( 109 | filter(lambda id: id in treeDic and len(treeDic[id]) >= lower and len(treeDic[id]) <= upper, fold_x)) 110 | self.treeDic = treeDic 111 | self.graph_path = './data/' + dataname + '_Interaction/' 112 | self.ego_path = './data/' + dataname + '_Ego/' 113 | self.droprate = droprate 114 | 115 | def __len__(self): 116 | return len(self.fold_x) 117 | 118 | def __getitem__(self, index): 119 | id = self.fold_x[index] 120 | data = np.load(self.graph_path + str(id) + '.npz', allow_pickle=True) 121 | edgeindex = data['edgeindex'] 122 | 123 | row,col = root_edge_enhance(list(edgeindex[0]),list(edgeindex[1])) 124 | 125 | burow = list(col) 126 | bucol = list(row) 127 | row.extend(burow) 128 | col.extend(bucol) 129 | if self.droprate > 0: 130 | length = len(row) 131 | poslist = random.sample(range(length), int(length * (1 - self.droprate))) 132 | poslist = sorted(poslist) 133 | row = list(np.array(row)[poslist]) 134 | col = list(np.array(col)[poslist]) 135 | new_edgeindex = [row, col] 136 | 137 | 138 | """ 139 | 自我中心网络 140 | """ 141 | user_ego = np.load(self.ego_path + str(id) + '.npz', allow_pickle=True) 142 | ego_twitter_id = id 143 | ego_root_feature = np.array(eval(str(user_ego['root_feature']))) 144 | ego_tree_feature = np.array(eval(str(user_ego['tree_feature']))) 145 | ego_edge_index = np.array(eval(str(user_ego['edge_index']))) 146 | ego_root_index = user_ego['root_index'] 147 | # ego_user_id = user_ego[5] 148 | 149 | """ 150 | end 151 | """ 152 | 153 | return Data(x=torch.tensor(data['x'], dtype=torch.float32), 154 | edge_index=torch.LongTensor(new_edgeindex), 155 | y=torch.LongTensor([int(data['y'])]), root=torch.LongTensor(data['root']), 156 | rootindex=torch.LongTensor([int(data['rootindex'])])) , \ 157 | Data(x=torch.tensor(ego_tree_feature, dtype=torch.float32), 158 | edge_index=torch.LongTensor(ego_edge_index), y=torch.LongTensor([int(data['y'])]), root=torch.LongTensor([ego_root_feature]), 159 | rootindex=torch.LongTensor([int(ego_root_index)]), tree_text_id=torch.LongTensor([int(ego_twitter_id)])) 160 | 161 | 162 | 163 | def root_edge_enhance(row,col): 164 | 165 | # 获取节点序列结合 166 | 167 | c = set(row).union(set(col)) 168 | 169 | sorted_list = sorted(c) 170 | 171 | # 获取补充的顶点对 172 | 173 | if sorted_list[0] != 0: 174 | return row,col 175 | 176 | new_row = [] 177 | new_col = [] 178 | for element in sorted_list[1:]: 179 | new_row.append(0) 180 | new_col.append(element) 181 | 182 | # row中找出为0元素的位置 183 | indices_row = [index for index, value in enumerate(row) if value == 0] 184 | 185 | # 去除a，b中对应索引位置的元素 186 | row = [row[i] for i in range(len(row)) if i not in indices_row] 187 | col = [col[i] for i in range(len(col)) if i not in indices_row] 188 | 189 | # col中找出0元素的位置 190 | 191 | indices_col = [index for index, value in enumerate(col) if value == 0] 192 | # 去除a，b中对应索引位置的元素 193 | row = [row[i] for i in range(len(row)) if i not in indices_col] 194 | col = [col[i] for i in range(len(col)) if i not in indices_col] 195 | 196 | row.extend(new_row) 197 | col.extend(new_col) 198 | return row,col -------------------------------------------------------------------------------- /data/Twitter16/Twitter16_Ego_Relationships.csv: -------------------------------------------------------------------------------- 1 | user_id friends_id 2 | 2377815434 [921473238756098049, 859889104045080583] 3 | 5402612 [265902729, 742143, 612473] 4 | 87416722 [113050195, 7587032] 5 | 14075928 [1054496392675319810, 133723860, 326507914, 262323041, 159894847] 6 | 1647857366 [2161705224, 466645191, 2805971402] 7 | 366355851 [22795629, 1382713221656694788, 4337569159, 2463548605, 25901146] 8 | 4714915461 [499571150] 9 | 1018225862 [762330058774151168, 4906190919, 43365216, 385509086, 466645191] 10 | 2326482060 [2608641372] 11 | 247399641 [1223297836151975937, 604070848, 15322606, 2371680043, 731753429782036480, 808105990579257349] 12 | 2677214379 [763055794703200257, 3081013470, 1621337209] 13 | 151896622 [88802201, 58711646] 14 | 1406346067 [301076357, 2193607094, 2376042180, 24987045, 342165503, 143516375] 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