├── .idea
├── HWNN.iml
├── encodings.xml
├── inspectionProfiles
│ └── Project_Default.xml
├── misc.xml
├── modules.xml
└── workspace.xml
├── HWNN.py
├── data.py
├── data
├── cora
│ ├── README
│ ├── cora.cites
│ └── cora.content
├── dblp
│ ├── dblp.cites
│ ├── dblp.content
│ ├── dblp_format.py
│ └── origin
│ │ ├── dblp_labels.txt
│ │ └── readme.md
└── pubmed
│ ├── Pubmed-Diabetes.DIRECTED.cites.tab
│ ├── Pubmed-Diabetes.NODE.paper.tab
│ ├── README
│ ├── light
│ └── sample.py
│ ├── pubmed.cites
│ ├── pubmed.content
│ ├── pubmed_format.py
│ └── raw_content.txt
└── readme.md
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/HWNN.py:
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1 | #!usr/bin/env python
2 | # -*- coding:utf-8 _*-
3 | """
4 | @project:HWNN
5 | @author:xiangguosun
6 | @contact:sunxiangguodut@qq.com
7 | @website:http://blog.csdn.net/github_36326955
8 | @file: HWNN.py
9 | @platform: macOS High Sierra 10.13.1 Pycharm pro 2017.1
10 | @time: 2019/10/16
11 | """
12 | import os
13 | import torch
14 | import torch.nn.functional as F
15 | import time
16 | from sklearn import metrics
17 | from sklearn.model_selection import train_test_split
18 | import argparse
19 |
20 | os.environ["CUDA_VISIBLE_DEVICES"] = "0"
21 |
22 |
23 | def parameter_parser():
24 | parser = argparse.ArgumentParser(description="Run HWNN.")
25 |
26 | parser.add_argument("--epochs",
27 | type=int,
28 | default=300, # 600
29 | help="Number of training epochs. Default is 300.")
30 |
31 | parser.add_argument("--filters",
32 | type=int,
33 | default=128,
34 | help="Filters (neurons) in convolution. Default is 128.")
35 |
36 | parser.add_argument("--test-size",
37 | type=float,
38 | default=0.2,
39 | help="Ratio of training samples. Default is 0.8")
40 |
41 | parser.add_argument("--dropout",
42 | type=float,
43 | default=0.01,
44 | help="Dropout probability. Default is 0.01")
45 |
46 | parser.add_argument("--seed",
47 | type=int,
48 | default=42,
49 | help="Random seed for sklearn pre-training. Default is 42.")
50 |
51 | parser.add_argument("--learning-rate",
52 | type=float,
53 | default=0.001,
54 | help="Learning rate. Default is 0.001.")
55 |
56 | parser.add_argument("--weight-decay",
57 | type=float,
58 | default=0.0001,
59 | help="Adam weight decay. Default is 0.0001.")
60 |
61 | return parser.parse_args()
62 |
63 |
64 | class HWNNLayer(torch.nn.Module):
65 | def __init__(self, in_channels, out_channels, ncount, device, K1=2, K2=2, approx=False, data=None):
66 | super(HWNNLayer, self).__init__()
67 | self.data = data
68 | self.in_channels = in_channels
69 | self.out_channels = out_channels
70 | self.ncount = ncount
71 | self.device = device
72 | self.K1 = K1
73 | self.K2 = K2
74 | self.approx = approx
75 | self.weight_matrix = torch.nn.Parameter(torch.Tensor(self.in_channels, self.out_channels))
76 | self.diagonal_weight_filter = torch.nn.Parameter(torch.Tensor(self.ncount))
77 | self.par = torch.nn.Parameter(torch.Tensor(self.K1 + self.K2))
78 | self.init_parameters()
79 |
80 | def init_parameters(self):
81 | torch.nn.init.xavier_uniform_(self.weight_matrix)
82 | torch.nn.init.uniform_(self.diagonal_weight_filter, 0.99, 1.01)
83 | torch.nn.init.uniform_(self.par, 0, 0.99)
84 |
85 | def forward(self, features, snap_index, data):
86 | diagonal_weight_filter = torch.diag(self.diagonal_weight_filter).to(self.device)
87 | features = features.to(self.device)
88 | # Theta=self.data.Theta.to(self.device)
89 | Theta = data.hypergraph_snapshot[snap_index]["Theta"].to(self.device)
90 | Theta_t = torch.transpose(Theta, 0, 1)
91 |
92 | if self.approx:
93 | poly = self.par[0] * torch.eye(self.ncount).to(self.device)
94 | Theta_mul = torch.eye(self.ncount).to(self.device)
95 | for ind in range(1, self.K1):
96 | Theta_mul = Theta_mul @ Theta
97 | poly = poly + self.par[ind] * Theta_mul
98 |
99 | poly_t = self.par[self.K1] * torch.eye(self.ncount).to(self.device)
100 | Theta_mul = torch.eye(self.ncount).to(self.device)
101 | for ind in range(self.K1 + 1, self.K1 + self.K2):
102 | Theta_mul = Theta_mul @ Theta_t # 这里也可以使用Theta_transpose
103 | poly_t = poly_t + self.par[ind] * Theta_mul
104 |
105 | # poly=self.par[0]*torch.eye(self.ncount).to(self.device)+self.par[1]*Theta+self.par[2]*Theta@Theta
106 | # poly_t = self.par[3] * torch.eye(self.ncount).to(self.device) + self.par[4] * Theta_t + self.par[5] * Theta_t @ Theta_t
107 | # poly_t = self.par[3] * torch.eye(self.ncount).to(self.device) + self.par[4] * Theta + self.par[
108 | # 5] * Theta @ Theta
109 | local_fea_1 = poly @ diagonal_weight_filter @ poly_t @ features @ self.weight_matrix
110 | else:
111 | print("wavelets!")
112 | wavelets = self.data.hypergraph_snapshot[snap_index]["wavelets"].to(self.device)
113 | wavelets_inverse = self.data.hypergraph_snapshot[snap_index]["wavelets_inv"].to(self.device)
114 | local_fea_1 = wavelets @ diagonal_weight_filter @ wavelets_inverse @ features @ self.weight_matrix
115 |
116 | localized_features = local_fea_1
117 | return localized_features
118 |
119 |
120 | class HWNN(torch.nn.Module):
121 | def __init__(self, args, ncount, feature_number, class_number, device, data):
122 | super(HWNN, self).__init__()
123 | self.args = args
124 | # self.features=features
125 | self.ncount = ncount
126 | self.feature_number = feature_number
127 |
128 | self.class_number = class_number
129 | self.device = device
130 | self.data = data
131 |
132 | self.hyper_snapshot_num = len(self.data.hypergraph_snapshot)
133 | print("there are {} hypergraphs".format(self.hyper_snapshot_num))
134 |
135 |
136 | self.setup_layers()
137 |
138 | self.par = torch.nn.Parameter(torch.Tensor(self.hyper_snapshot_num))
139 | torch.nn.init.uniform_(self.par, 0, 0.99) # 1.0)
140 |
141 | def setup_layers(self):
142 | self.convolution_1 = HWNNLayer(self.feature_number,
143 | self.args.filters,
144 | self.ncount,
145 | self.device,
146 | K1=3,
147 | K2=3,
148 | approx=True,
149 | data=self.data)
150 |
151 | self.convolution_2 = HWNNLayer(self.args.filters,
152 | self.class_number,
153 | self.ncount,
154 | self.device,
155 | K1=3,
156 | K2=3,
157 | approx=True,
158 | data=self.data)
159 |
160 | def forward(self, features):
161 | features = features.to(self.device)
162 | channel_feature = []
163 | for snap_index in range(self.hyper_snapshot_num):
164 | deep_features_1 = F.relu(self.convolution_1(features,
165 | snap_index,
166 | self.data))
167 | deep_features_1 = F.dropout(deep_features_1, self.args.dropout)
168 | deep_features_2 = self.convolution_2(deep_features_1,
169 | snap_index,
170 | self.data)
171 | deep_features_2 = F.log_softmax(deep_features_2, dim=1) # 把这里换成relu会怎么样呢?
172 | channel_feature.append(deep_features_2)
173 |
174 | deep_features_3 = torch.zeros_like(channel_feature[0])
175 | for ind in range(self.hyper_snapshot_num):
176 | deep_features_3 = deep_features_3 + self.par[ind] * channel_feature[ind]
177 |
178 | return deep_features_3
179 |
180 |
181 | class HWNNTrainer(object):
182 | def __init__(self, args, features, target, data):
183 | self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
184 | print(self.device)
185 | self.args = args
186 | self.data = data
187 |
188 | self.features = features
189 | self.ncount = self.features.size()[0]
190 | self.feature_number = self.features.size()[1]
191 |
192 | self.target = target.to(self.device)
193 | self.class_number = self.data.class_num
194 |
195 | self.setup_model()
196 | self.train_test_split()
197 |
198 | def setup_model(self):
199 | self.model = HWNN(self.args,
200 | self.ncount,
201 | self.feature_number,
202 | self.class_number,
203 | self.device, self.data).to(self.device)
204 |
205 | def train_test_split(self):
206 | nodes = [x for x in range(self.ncount)]
207 | train_nodes, test_nodes = train_test_split(nodes, test_size=self.args.test_size, random_state=self.args.seed)
208 | self.train_nodes = torch.LongTensor(train_nodes)
209 | self.test_nodes = torch.LongTensor(test_nodes)
210 |
211 | def fit(self):
212 | print("HWNN Training.\n")
213 |
214 | self.optimizer = torch.optim.Adam(self.model.parameters(), lr=self.args.learning_rate,
215 | weight_decay=self.args.weight_decay)
216 | self.model.train()
217 | self.best_accuracy = 0.0
218 | self.best_micro_f1 = 0.0
219 | self.best_macro_f1 = 0.0
220 | self.best_precision = 0.0
221 | self.best_recall = 0.0
222 | # writer = SummaryWriter()
223 |
224 | for epoch in range(self.args.epochs):
225 | self.time = time.time()
226 | self.optimizer.zero_grad()
227 |
228 | prediction = self.model(self.features)
229 |
230 | loss_train = torch.nn.functional.cross_entropy(prediction[self.train_nodes],
231 | self.target[self.train_nodes])
232 |
233 | loss_test = torch.nn.functional.cross_entropy(prediction[self.test_nodes],
234 | self.target[self.test_nodes])
235 |
236 | loss_train.backward()
237 | self.optimizer.step()
238 |
239 | _, prediction = self.model(self.features).max(dim=1)
240 |
241 | """
242 | self.target[self.test_nodes].cpu()
243 | tensor([2, 2, 1, ..., 5, 2, 0])
244 |
245 | prediction[self.test_nodes].cpu()
246 | tensor([6, 0, 2, ..., 6, 2, 0])
247 | """
248 |
249 | # # accuracy
250 | correct_test = prediction[self.test_nodes].eq(self.target[self.test_nodes]).sum().item()
251 | accuracy_test = correct_test / int(self.ncount * self.args.test_size)
252 | correct_train = prediction[self.train_nodes].eq(self.target[self.train_nodes]).sum().item()
253 | accuracy_train = correct_train / int(self.ncount * (1 - self.args.test_size))
254 |
255 | # micro F1
256 | """
257 | >>> y_true = [0, 1, 2, 0, 1, 2]
258 | >>> y_pred = [0, 2, 1, 0, 0, 1]
259 | """
260 |
261 | micro_f1 = metrics.f1_score(self.target[self.test_nodes].cpu(),
262 | prediction[self.test_nodes].cpu(),
263 | average='micro')
264 |
265 | # macro F1
266 | macro_f1 = metrics.f1_score(self.target[self.test_nodes].cpu(),
267 | prediction[self.test_nodes].cpu(),
268 | average='macro')
269 |
270 | # precision
271 | precision = metrics.precision_score(self.target[self.test_nodes].cpu(),
272 | prediction[self.test_nodes].cpu(),
273 | average='macro')
274 | # recall
275 | recall = metrics.recall_score(self.target[self.test_nodes].cpu(),
276 | prediction[self.test_nodes].cpu(),
277 | average='macro')
278 |
279 | if self.best_accuracy < accuracy_test:
280 | self.best_accuracy = accuracy_test
281 |
282 | if self.best_micro_f1 < micro_f1:
283 | self.best_micro_f1 = micro_f1
284 | if self.best_macro_f1 < macro_f1:
285 | self.best_macro_f1 = macro_f1
286 | if self.best_precision < precision:
287 | self.best_precision = precision
288 | if self.best_recall < recall:
289 | self.best_recall = recall
290 |
291 | print("epo:{}/{}|"
292 | "train_los:{:.4f}|"
293 | "test_los:{:.4f}|"
294 | "train_acc:{:.4f}|"
295 | "test_acc:{:.4f}|"
296 | "best_acc:{:.4f}|"
297 | "best_micro_f1:{:.4f}|"
298 | "best_macro_f1:{:.4f}|"
299 | "best_precision:{:.4f}|"
300 | "best_recall:{:.4f}".format(epoch, self.args.epochs,
301 | loss_train,
302 | loss_test,
303 | accuracy_train,
304 | accuracy_test,
305 | self.best_accuracy,
306 | self.best_micro_f1,
307 | self.best_macro_f1,
308 | self.best_precision,
309 | self.best_recall))
310 |
311 |
312 | if __name__ == "__main__":
313 | from data import Data
314 |
315 | args = parameter_parser()
316 | data = Data()
317 | # data.load(data_path='./data/spammer/',data_name='spammer')
318 | data.load(data_path='./data/cora/', data_name='cora')
319 |
320 | target = data.nodes_labels_sequence.type(torch.LongTensor)
321 | features = data.X_0.type(torch.FloatTensor)
322 |
323 | trainer = HWNNTrainer(args, features, target, data)
324 | trainer.fit()
325 |
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/data.py:
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1 | #!usr/bin/env python
2 | # -*- coding:utf-8 _*-
3 | """
4 | @project:HWNN
5 | @author:xiangguosun
6 | @contact:sunxiangguodut@qq.com
7 | @website:http://blog.csdn.net/github_36326955
8 | @file: data.py
9 | @platform: macOS High Sierra 10.13.1 Pycharm pro 2017.1
10 | @time: 2019/10/16
11 | """
12 | import numpy as np
13 | from collections import defaultdict
14 | import torch
15 |
16 |
17 | class Data:
18 | def __init__(self, metapathscheme=None):
19 | self.data_path = None
20 | self.nodes_labels = None # one-hot torch matrix
21 | self.nodes_names_int = None # list, int
22 | self.nodes_names_map = None # content和cite节点的顺序不一样,为此通过这个进行映射查找
23 | self.X_0 = None
24 | self.class_num = 0 # first initialed in def _label_string2matrix
25 | self.nodes_labels_sequence = None # node labels like [1,2,5,3,0]. please note the differece with self.nodes_labels
26 | self.edges = None # simple graph edges (src,des,edge_type),
27 | self.nodes_number = 0
28 | self.s = 1.0
29 | self.hypergraph_snapshot = []
30 | self.simplegraph_snapshot = []
31 | self.metapathscheme = metapathscheme # [[0,0]]
32 | self.labeled_node_index = [] # only valid for imdb dataset
33 | """
34 | We set self.hypergraph_snapshot[0],
35 | and self.simplegraph_snapshot[0] as the compelete hypergraph/simple graph
36 | by default.
37 |
38 | for cora dataset, it is actually a homogeneous hypergraph. here we just copy the
39 | hypergraph three times, and generate three same hypergraph snapshots.
40 | """
41 |
42 | def _label_string2matrix(self, nodes_labels_str):
43 | b, c = np.unique(nodes_labels_str, return_inverse=True)
44 | class_num = len(b)
45 | sample_num = len(c)
46 | self.class_num = class_num
47 | self.nodes_labels_sequence = torch.from_numpy(c)
48 | nodes_labels = torch.zeros((sample_num, class_num))
49 | i = 0
50 | for la in c:
51 | nodes_labels[i, la] = 1.0
52 | i = i + 1
53 | return nodes_labels
54 |
55 | def _nodes_names_map(self, nodes_names_int):
56 | """
57 | 我们约定:所有涉及到节点的字典类型,全部是节点的nodes name,str
58 | 所有涉及到节点的矩阵,全部是节点的index,即nodes_names_map[node name]
59 | note that all dic data with nodes name(str),
60 | all matrix data with nodes index (int, nodes_names_map[node name])
61 | """
62 | nodes_names_map = defaultdict(int)
63 | i = 0
64 | for node_name in nodes_names_int:
65 | nodes_names_map[str(node_name)] = i
66 | i = i + 1
67 | return nodes_names_map
68 |
69 | def _hypergraph_cora(self, edges):
70 | """
71 | The following hypergraph is neibhbor-based (1-hop) hypergraph, to generate other kinds of hypergraphs
72 | please refer the source code of paper:
73 | Hongxu Chen et al.Multi-level Graph Convolutional Networks for Cross-platform Anchor Link Prediction. KDD2020
74 | code: https://github.com/sunxiangguo/MGCN
75 |
76 | 'Attribute-based'
77 | # att_col=pickle.load(open('./data/cora/attribute_col_sum.list', 'rb')) # (超边按照单词的频率降序)
78 | # print([(i, x) for i, x in enumerate(att_col)])
79 | H_att = torch.load('./data/cora/attribute_1433.H')[:, 0:1000]
80 | # print(H_att.shape)
81 | 'Cluster-based'
82 | H_clu = torch.load('./data/cora/clusters_100.H')
83 | # print(H_clu.shape)
84 | 'Community-based'
85 | H_com = torch.load('./data/cora/community_28.H')
86 | # print(H_com.shape)
87 | 'K-nearest_pro/
88 | H_knp=torch.load('./data/cora/k_10_nearest_pro.H').float()
89 |
90 | 'K-nearest_int'
91 | H_kni = torch.load('./data/cora/k_10_nearest_int.H').float()
92 | """
93 |
94 | graph = defaultdict(list)
95 | for edge in edges:
96 | graph[edge[0]].append(edge[0])
97 | graph[edge[0]].append(edge[1])
98 | graph[edge[1]].append(edge[1])
99 | graph[edge[1]].append(edge[0])
100 | # 去重复, unique
101 | for item in graph.items():
102 | graph[item[0]] = np.unique(item[1])
103 |
104 | indice_matrix = torch.zeros((self.nodes_number, len(graph.keys())))
105 | # column_names = self.hyperedges.keys()
106 | col = 0
107 | for hyperedge in graph.items():
108 | for node in hyperedge[1]:
109 | row = self.nodes_names_map[node]
110 | indice_matrix[row, col] = 1
111 | col = col + 1
112 |
113 | W_e_diag = torch.ones(indice_matrix.size()[1])
114 |
115 | D_e_diag = torch.sum(indice_matrix, 0)
116 | D_e_diag = D_e_diag.view((D_e_diag.size()[0]))
117 |
118 | D_v_diag = indice_matrix.mm(W_e_diag.view((W_e_diag.size()[0]), 1))
119 | D_v_diag = D_v_diag.view((D_v_diag.size()[0]))
120 |
121 | Theta = torch.diag(torch.pow(D_v_diag, -0.5)) @ \
122 | indice_matrix @ torch.diag(W_e_diag) @ \
123 | torch.diag(torch.pow(D_e_diag, -1)) @ \
124 | torch.transpose(indice_matrix, 0, 1) @ \
125 | torch.diag(torch.pow(D_v_diag, -0.5))
126 |
127 | Theta_inverse = torch.pow(Theta, -1)
128 | Theta_inverse[Theta_inverse == float("Inf")] = 0
129 |
130 | Theta_I = torch.diag(torch.pow(D_v_diag, -0.5)) @ \
131 | indice_matrix @ torch.diag(W_e_diag + torch.ones_like(W_e_diag)) @ \
132 | torch.diag(torch.pow(D_e_diag, -1)) @ \
133 | torch.transpose(indice_matrix, 0, 1) @ \
134 | torch.diag(torch.pow(D_v_diag, -0.5))
135 |
136 | Theta_I[Theta_I != Theta_I] = 0
137 | Theta_I_inverse = torch.pow(Theta_I, -1)
138 | Theta_I_inverse[Theta_I_inverse == float("Inf")] = 0
139 |
140 | Laplacian = torch.eye(Theta.size()[0]) - Theta
141 |
142 | fourier_e, fourier_v = torch.symeig(Laplacian, eigenvectors=True)
143 | # fourier_e, fourier_v = np.linalg.eig(Laplacian)
144 |
145 | wavelets = fourier_v @ torch.diag(torch.exp(-1.0 * fourier_e * self.s)) @ torch.transpose(fourier_v, 0, 1)
146 | wavelets_inv = fourier_v @ torch.diag(torch.exp(fourier_e * self.s)) @ torch.transpose(fourier_v, 0, 1)
147 | wavelets_t = torch.transpose(wavelets, 0, 1)
148 | # 根据那篇论文的评审意见,这里用wavelets_t或许要比wavelets_inv效果更好?
149 |
150 | wavelets[wavelets < 0.00001] = 0
151 | wavelets_inv[wavelets_inv < 0.00001] = 0
152 | wavelets_t[wavelets_t < 0.00001] = 0
153 |
154 | hypergraph = {"graph": graph,
155 | "indice_matrix": indice_matrix,
156 | "D_v_diag": D_v_diag,
157 | "D_e_diag": D_e_diag,
158 | "W_e_diag": W_e_diag, # hyperedge_weight_flat
159 | "laplacian": Laplacian,
160 | "fourier_v": fourier_v,
161 | "fourier_e": fourier_e,
162 | "wavelets": wavelets,
163 | "wavelets_inv": wavelets_inv,
164 | "wavelets_t": wavelets_t,
165 | "Theta": Theta,
166 | "Theta_inv": Theta_inverse,
167 | "Theta_I": Theta_I,
168 | "Theta_I_inv": Theta_I_inverse,
169 | }
170 | return hypergraph
171 |
172 | def _simplegraph_cora(self, edges):
173 | graph = defaultdict(list)
174 |
175 | for edge in edges: # 这里的key和value均存的是node_name(str),不是node_index(按照content的顺序从0开始编号,int)
176 | graph[edge[0]].append(edge[1])
177 | graph[edge[1]].append(edge[0])
178 | # 去重复,和loop,求D_v
179 | node_degree_flat = torch.zeros(self.nodes_number)
180 | A = torch.zeros((self.nodes_number, self.nodes_number))
181 | for item in graph.items():
182 | if item[0] in item[1]:
183 | item[1].remove(item[0])
184 | graph[item[0]] = np.unique(item[1])
185 | node_degree_flat[self.nodes_names_map[item[0]]] = len(graph[item[0]])
186 | for node in graph[item[0]]:
187 | A[self.nodes_names_map[item[0]], self.nodes_names_map[node]] = 1
188 |
189 | # laplacian
190 | node_degree_flat_pow = torch.pow(node_degree_flat, -0.5)
191 | node_degree_flat_pow[node_degree_flat_pow == float("Inf")] = 0 # replace inf with 0
192 | node_degree_flat_pow[node_degree_flat_pow != node_degree_flat_pow] = 0 # replace nan with 0
193 |
194 | L = torch.eye(self.nodes_number) - torch.diag(node_degree_flat_pow) @ A @ torch.diag(node_degree_flat_pow)
195 | fourier_e, fourier_v = torch.symeig(L, eigenvectors=True)
196 |
197 | # wavelets
198 | wavelets = fourier_v @ torch.diag(torch.exp(-1.0 * fourier_e * self.s)) @ torch.transpose(fourier_v, 0, 1)
199 | wavelets_inv = fourier_v @ torch.diag(torch.exp(fourier_e * self.s)) @ torch.transpose(fourier_v, 0, 1)
200 | wavelets[wavelets < 0.00001] = 0
201 | wavelets_inv[wavelets_inv < 0.00001] = 0
202 |
203 | # metapath_list
204 | # metapath_list=[[0,0]]
205 | # node_type_map
206 | node_type_list = [0]
207 | node_type_map = defaultdict(int) # {'1':0}
208 | type_node_map = defaultdict(list) # {0:['1','3','4']}
209 | for node, _ in graph.items():
210 | type_node_map[0].append(node)
211 |
212 | for item in graph.items():
213 | node_type_map[item[0]] = 0
214 |
215 | simple_graph = {"graph": graph, # {'1':['2','6','10'],}
216 | "edges": edges, # numpy array
217 | "node_type_list": node_type_list,
218 | # "metapath_list":metapath_list,
219 | "node_degree_flat": node_degree_flat,
220 | "node_type_map": node_type_map,
221 | "type_node_map": type_node_map,
222 | "adj": A,
223 | "laplacian": L,
224 | "fourier_e": fourier_e,
225 | "fourier_v": fourier_v,
226 | "wavelets": wavelets,
227 | "wavelets_inv": wavelets_inv}
228 | return simple_graph
229 |
230 | def load(self, data_path, data_name, save=False):
231 |
232 | print('start loading...')
233 | self.data_path = data_path # "../data/cora/
234 | content = np.loadtxt(self.data_path + data_name + ".content", dtype=str)
235 | # print("content\n",content)
236 |
237 | # labels
238 | nodes_labels_str = content[:, -1] # str
239 | self.nodes_labels = self._label_string2matrix(nodes_labels_str)
240 | self.nodes_number = self.nodes_labels.size()[0]
241 | # print("self.nodes_number\n",self.nodes_number)
242 |
243 | # node names
244 | self.nodes_names_int = content[:, 0].astype(np.str) # .astype(np.int) # int
245 | """
246 | this is a bad variable name, nodes_names_int acturally is str type, not int
247 | this confusion was caused by some history reasons. please be careful
248 | """
249 | print('creating a node map...')
250 | self.nodes_names_map = self._nodes_names_map(self.nodes_names_int)
251 |
252 | # feature matrix
253 | nodes_features_int = content[:, 1:-1].astype(np.float) # int
254 | print('constructing feature matrix...')
255 | # sparsity = np.sum(self.nodes_features == 1) * 100.0 / self.nodes_features.size
256 | self.X_0 = torch.from_numpy(nodes_features_int)
257 | # print(self.X_0)
258 | # print(self.X_0.shape)
259 |
260 | # edges
261 | print('loading edges...')
262 | self.edges = np.loadtxt(self.data_path + "/" + data_name + ".cites", dtype=str)
263 | # print(self.edges)
264 | # print(self.edges.shape)
265 | """
266 | default format:
267 | cora.cites
268 | src\tdes\ttype
269 | """
270 | if data_name in ["cora", 'pubmed', 'aminer', 'spammer']:
271 | """
272 | compelete simple graph
273 | """
274 | print("construct simple graphs...")
275 | simple_graph = self._simplegraph_cora(self.edges)
276 | self.simplegraph_snapshot.append(simple_graph)
277 |
278 | """
279 | simple graph snapshots, you just need to remove unrelated edges in self.edges, and send
280 | them into the same function
281 | simple_graph = self._simplegraph_cora(self.edges)
282 | self.simplegraph_snapshot.append(simple_graph)
283 | """
284 |
285 | print("construct hypergraphs...")
286 | hypergraph = self._hypergraph_cora(self.edges)
287 | self.hypergraph_snapshot.append(hypergraph)
288 | self.hypergraph_snapshot.append(hypergraph)
289 | self.hypergraph_snapshot.append(hypergraph)
290 | print("load done!")
291 | elif data_name == "dblp" or data_name == "imdb":
292 | """
293 | compelete simple graph
294 | """
295 | print("construct simple graphs...")
296 | simple_graph = self._simplegraph_dblp(self.edges)
297 | self.simplegraph_snapshot.append(simple_graph)
298 |
299 | print("construct hypergraphs...")
300 |
301 | hypergraph = self._hypergraph_dblp(self.edges)
302 | self.hypergraph_snapshot.append(hypergraph)
303 | self.hypergraph_snapshot.append(hypergraph)
304 | # self.hypergraph_snapshot.append(hypergraph)
305 |
306 | print("load done!")
307 |
308 |
309 | if __name__ == "__main__":
310 | data = Data()
311 | # data.load(data_path='./data/spammer/', data_name='spammer')
312 | data.load(data_path='./data/cora/', data_name='cora')
313 |
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/data/cora/README:
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1 | This directory contains the a selection of the Cora dataset (www.research.whizbang.com/data).
2 |
3 | The Cora dataset consists of Machine Learning papers. These papers are classified into one of the following seven classes:
4 | Case_Based
5 | Genetic_Algorithms
6 | Neural_Networks
7 | Probabilistic_Methods
8 | Reinforcement_Learning
9 | Rule_Learning
10 | Theory
11 |
12 | The papers were selected in a way such that in the final corpus every paper cites or is cited by atleast one other paper. There are 2708 papers in the whole corpus.
13 |
14 | After stemming and removing stopwords we were left with a vocabulary of size 1433 unique words. All words with document frequency less than 10 were removed.
15 |
16 |
17 | THE DIRECTORY CONTAINS TWO FILES:
18 |
19 | The .content file contains descriptions of the papers in the following format:
20 |
21 | +
22 |
23 | The first entry in each line contains the unique string ID of the paper followed by binary values indicating whether each word in the vocabulary is present (indicated by 1) or absent (indicated by 0) in the paper. Finally, the last entry in the line contains the class label of the paper.
24 |
25 | The .cites file contains the citation graph of the corpus. Each line describes a link in the following format:
26 |
27 |
28 |
29 | Each line contains two paper IDs. The first entry is the ID of the paper being cited and the second ID stands for the paper which contains the citation. The direction of the link is from right to left. If a line is represented by "paper1 paper2" then the link is "paper2->paper1".
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/data/dblp/dblp.cites:
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2 | p303 a173 0
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70 | p141 c1 1
71 | p192 c1 1
72 | p303 c1 1
73 | p381 c1 1
74 | p433 c1 1
75 | p462 c1 1
76 | p706 c1 1
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88 | p2494 c3 1
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90 | p2577 c4 1
91 | p2627 c4 1
92 | p2714 c4 1
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94 | p2789 c5 1
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96 | p3480 c7 1
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98 | p3521 c7 1
99 | p3597 c7 1
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166 | p13374 c18 1
167 | p13458 c18 1
168 | p13656 c18 1
169 | p14298 c19 1
170 |
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176 | c11 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2
177 | c12 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2
178 | c14 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
179 | c15 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2
180 | c16 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4
181 | c17 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
182 | c18 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
183 | c19 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4
184 |
--------------------------------------------------------------------------------
/data/dblp/dblp_format.py:
--------------------------------------------------------------------------------
1 | #!usr/bin/env python
2 | # -*- coding:utf-8 _*-
3 | """
4 | @project:HWNN
5 | @author:xiangguosun
6 | @contact:sunxiangguodut@qq.com
7 | @website:http://blog.csdn.net/github_36326955
8 | @file: dblp_format.py
9 | @platform: macOS High Sierra 10.13.1 Pycharm pro 2017.1
10 | @time: 2019/10/16
11 |
12 |
13 | dblp.cites
14 | src\tdes\ttype
15 | p9896 a6716 0
16 | p c 1
17 |
18 | dblp.content
19 | p 临街矩阵 标签
20 | a 临街矩阵 标签
21 |
22 | 请注意:content里的内容不能直接从原始文件中拷贝,
23 | 因为有些节点可能是孤立的,因此要从原始的label文件里抽取
24 | 抽取的依据是他们在边文件中是否存在
25 | """
26 | import numpy as np
27 | from collections import defaultdict
28 | import torch
29 |
30 |
31 | dblp_labels=np.loadtxt('./origin/dblp_labels.txt',dtype=str)
32 | all_nodes_names=list(dblp_labels[:,0])
33 |
34 |
35 | dblp=np.loadtxt('./origin/dblp.txt',dtype=str)
36 |
37 | nodes_set=set()
38 |
39 | with open('./dblp.cites','w') as fout:
40 | for row in dblp:
41 | if row[0] in all_nodes_names and row[1] in all_nodes_names:
42 | nodes_set.add(row[0])
43 | nodes_set.add(row[1])
44 | if 'a' in row[1]:
45 | edge_type='0'
46 | elif 'c' in row[1]:
47 | edge_type='1'
48 | else:
49 | break
50 | fout.write(row[0]+'\t'+row[1]+'\t'+edge_type+'\n')
51 |
52 |
53 |
54 | dblp=np.loadtxt('./dblp.cites',dtype=str)
55 | # print(dblp)
56 | # print(dblp_labels)
57 |
58 |
59 |
60 |
61 |
62 |
63 |
64 |
65 |
66 | filter_labels=[]
67 | for row in dblp_labels:
68 | if row[0] in nodes_set:
69 | filter_labels.append(row)
70 |
71 |
72 | dblp_labels=np.asarray(filter_labels)
73 | all_nodes_names=list(dblp_labels[:,0])
74 | graph=defaultdict(list)
75 | A=np.zeros((dblp_labels.shape[0],dblp_labels.shape[0]),dtype=np.int)
76 |
77 |
78 | nodes_names_map=defaultdict(int)
79 | i=0
80 | for row in dblp_labels:
81 | nodes_names_map[row[0]]=i
82 | i=i+1
83 | # print(nodes_names_map)
84 |
85 | for edge in dblp:
86 | # print(edge,nodes_names_map[edge[0]],nodes_names_map[edge[1]])
87 | A[nodes_names_map[edge[0]],nodes_names_map[edge[1]]]=1
88 | A[nodes_names_map[edge[1]], nodes_names_map[edge[0]]] = 1
89 | # A=A.astype(str)
90 | content=np.concatenate((dblp_labels[:,0].reshape(-1,1),A,dblp_labels[:,1].reshape(-1,1)),axis=1)
91 | print(content)
92 | np.savetxt('./dblp.content',content,fmt='%s',delimiter='\t')
93 | # for edge in dblp:
94 | # graph[edge[0]].append(edge[1])
95 | # graph[edge[1]].append(edge[0])
96 | #
97 | # print(graph)
98 | # for item in graph.items():
99 | # if item[0] in item[1]:
100 | # item[1].remove(item[0])
101 | # graph[item[0]] = np.unique(item[1])
102 | # for node in graph[item[0]]:
103 | # print(nodes_names_map[item[0]],nodes_names_map[node])
104 | # A[nodes_names_map[item[0]], nodes_names_map[node]] = 1
105 | #
106 | # print(A)
107 |
108 |
109 |
110 |
111 |
--------------------------------------------------------------------------------
/data/dblp/origin/dblp_labels.txt:
--------------------------------------------------------------------------------
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2 | a4 3
3 | a5 4
4 | a13 3
5 | a14 2
6 | a15 3
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4149 | p12714 1
4150 | p12862 1
4151 | p13088 1
4152 | p13115 1
4153 | p13341 1
4154 | p13374 1
4155 | p13458 3
4156 | p13656 1
4157 | p14298 1
4158 | c1 3
4159 | c2 4
4160 | c3 3
4161 | c4 4
4162 | c5 3
4163 | c6 1
4164 | c7 1
4165 | c8 2
4166 | c9 3
4167 | c10 3
4168 | c11 2
4169 | c12 2
4170 | c13 2
4171 | c14 1
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4173 | c16 4
4174 | c17 1
4175 | c18 1
4176 | c19 4
4177 | c20 4
4178 |
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/data/dblp/origin/readme.md:
--------------------------------------------------------------------------------
1 | DBLP is a bibliographic network in computer science
2 | collected from four research areas: database, data
3 | mining, machine learning, and information retrieval. In
4 | the dataset, 4057 authors, 20 venues and 100 papers are
5 | labeled with one of the four research areas.
6 |
7 | Yizhou Sun, Jiawei Han, Xifeng Yan, Philip S Yu, and Tianyi Wu. 2011. Pathsim:
8 | Meta path-based top-k similarity search in heterogeneous information networks.
9 | Proceedings of the VLDB Endowment 4, 11 (2011), 992–1003.
10 |
11 |
12 |
13 | 这里有四种节点
14 | t:term单词,只与p相连
15 | a:author作者,有标签
16 | p:论文,有标签
17 | c:会议,有标签
18 |
19 | 边只有p-a,p-t,p-c这三种
20 |
21 | 处理措施
22 | 生成dblp.cites
23 | 移除p-t这一类型
24 | 生成dblp.content,将邻接矩阵作为属性,维度在4177,可以接受
25 |
--------------------------------------------------------------------------------
/data/pubmed/README:
--------------------------------------------------------------------------------
1 | The Pubmed dataset consists of 19717 scientific publications
2 | from PubMed database pertaining to diabetes classified
3 | into one of three classes
4 | ("Diabetes Mellitus, Experimental", "Diabetes Mellitus Type 1", "Diabetes Mellitus Type 2").
5 | The citation network consists of 44338 links. Each publication in
6 | the dataset is described by a TF/IDF weighted word vector from a
7 | dictionary which consists of 500 unique words.
8 |
9 | The files consists of tab delimited files where the first line
10 | describes the contents of the files and the second line describes
11 | the names and types of the attributes.
12 |
13 |
--------------------------------------------------------------------------------
/data/pubmed/light/sample.py:
--------------------------------------------------------------------------------
1 | #!usr/bin/env python
2 | # -*- coding:utf-8 _*-
3 | """
4 | @project:HWNN
5 | @author:xiangguosun
6 | @contact:sunxiangguodut@qq.com
7 | @website:http://blog.csdn.net/github_36326955
8 | @file: dblp_format.py
9 | @platform: macOS High Sierra 10.13.1 Pycharm pro 2017.1
10 | @time: 2019/10/16
11 |
12 | 16604个作者
13 | 第一步:doc2vec模型将author_interest文件转换为feature矩阵
14 | 第二部:Aminer_coauthor本身就是.cites文件
15 | 第三部:将author_class与feature拼接,构成.content文件
16 | """
17 |
18 | import numpy as np
19 | from collections import defaultdict
20 | import torch
21 | import random
22 | import os
23 | import pandas as pd
24 | from gensim.models.doc2vec import Doc2Vec, TaggedDocument
25 | #
26 | all_cites=np.loadtxt('../pubmed.cites',delimiter='\t',dtype=str)
27 |
28 | all_content=np.loadtxt('../pubmed.content',delimiter='\t',dtype=str)
29 |
30 | rand_arr = np.arange(all_cites.shape[0])
31 | np.random.shuffle(rand_arr)
32 | slide_capacity=4000
33 | cites=all_cites[rand_arr[0:slide_capacity]]
34 |
35 | node_set=set()
36 | for edge in cites:
37 | node_set.add(edge[0])
38 | node_set.add(edge[1])
39 |
40 |
41 | print("node number:",len(list(node_set)))
42 |
43 | content=[]
44 |
45 | for row in all_content:
46 | node=row[0]
47 | if node in node_set:
48 | content.append(row)
49 |
50 |
51 | np.savetxt('./pubmed.content', content, fmt='%s', delimiter='\t')
52 |
53 | with open('./pubmed.cites','w') as fout:
54 | for edge in cites:
55 | src,des=edge[0],edge[1]
56 | fout.write(src+'\t'+des+'\n')
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/data/pubmed/pubmed_format.py:
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1 | #!usr/bin/env python
2 | # -*- coding:utf-8 _*-
3 | """
4 | @project:HWNN
5 | @author:xiangguosun
6 | @contact:sunxiangguodut@qq.com
7 | @website:http://blog.csdn.net/github_36326955
8 | @file: dblp_format.py
9 | @platform: macOS High Sierra 10.13.1 Pycharm pro 2017.1
10 | @time: 2019/10/16
11 |
12 |
13 | """
14 | import numpy as np
15 | from collections import defaultdict
16 | import torch
17 |
18 | col_name_list=[]
19 | word_ind_map=defaultdict(int)
20 | with open('./raw_content.txt','r') as fin:
21 | lines=fin.readlines()
22 | first_line=lines[1]
23 | col_name=first_line.split('\t')
24 | word_ind=0
25 |
26 | for i in range(1,len(col_name)-1):
27 | word=col_name[i].split(':')[1]
28 | word_ind_map[word]=word_ind
29 | word_ind=word_ind+1
30 | print(len(word_ind_map.keys()))
31 | word_numer=len(word_ind_map.keys())
32 | sample_number=len(lines)-2
33 | features=np.zeros((sample_number,word_numer),dtype=float)
34 | node_list=[]
35 | label_list=[]
36 |
37 | line_number=0
38 | for line in lines[2:]:
39 | """
40 | 12187484 label=1 w-rat=0.09393489570187145 w-common=0.028698458467273157 summary=w-rat,w-studi
41 | """
42 | slices=line.split('\t')
43 | node_name=slices[0]
44 | node_label=slices[1].split('=')[1]
45 | node_list.append(node_name)
46 | label_list.append(node_label)
47 | for word_value in slices[2:-1]:
48 | [word,value]=word_value.split('=')
49 | features[line_number,word_ind_map[word]]=value
50 | line_number=line_number+1
51 | left=np.asarray(node_list).reshape(-1,1)
52 | right=np.asarray(label_list).reshape(-1,1)
53 | print(left.shape)
54 | print(right.shape)
55 | content=np.concatenate((left,features,right),axis=1)
56 | np.savetxt('./pubmed.content', content, fmt='%s', delimiter='\t')
57 |
58 |
59 |
60 |
61 |
62 |
63 |
64 |
65 |
66 |
67 |
68 |
69 |
70 |
71 | # raw_edges=np.loadtxt('./Pubmed-Diabetes.DIRECTED.cites.tab',delimiter='\t',dtype=str)
72 | #
73 | # print(raw_edges)
74 | # print(raw_edges.shape)
75 | # raw_edges=raw_edges[:,[1,3]]
76 | # # raw_edges=np.concatenate((raw_edges[:,1],raw_edges[:,3]),axis=1)
77 | # print(raw_edges)
78 | # print(raw_edges.shape)
79 | # with open('./pubmed.cites','w') as fout:
80 | # for line in raw_edges:
81 | # src=line[0].split(':')[1]
82 | # des=line[1].split(':')[1]
83 | # fout.write(src+'\t'+des+'\n')
84 |
85 |
86 |
87 |
88 |
89 |
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/readme.md:
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1 | This is the source code (beta version) of our paper:
2 | Xiangguo Sun et al. Heterogeneous Hypergraph Embedding for Graph Classification, WSDM2021
3 |
4 | A more advanced version will be released in the near future (around January, 2021).
5 |
6 |
7 | Datasets:
8 | Pumbed, Cora, DBLP are included in the folder 'data'
9 |
10 | For the Spammer dataset, please contact the authors of the following paper to obtain the permission:
11 | Bo Liu et al. Co-Detection of Crowdturfing Microblogs and Spammers in Online Social Networks. World Wide Web Journal (WWWJ). 2020, 23, 573–607
12 |
13 |
14 | Please cite our paper if possible:
15 |
16 | ```bibtex
17 | @inproceedings{sun2020hwnn,
18 | title={Heterogeneous Hypergraph Embedding for Graph Classification},
19 | author={Sun, Xiangguo and
20 | Yin, Hongzhi and
21 | Liu, Bo and
22 | Chen, Hongxu and
23 | Shao, Yingxia and
24 | Viet Hung, Nguyen Quoc},
25 | booktitle={14th ACM International Conference on Web Search and Data Mining (WSDM2021)},
26 | year={2021}
27 | }
28 | ```
29 |
30 |
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