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.gitignore

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+.idea/
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+pretrain_model.pk 
+# Byte-compiled / optimized / DLL files
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+*$py.class
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+
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+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
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+# mypy
+.mypy_cache/

LICENSE

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+MIT License
+
+Copyright (c) 2018 zfjsail
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

README.md

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+# CAN-pytorch
+This is a PyTorch implementation of the CAN model described in the paper:
+PyTorch version for CAN: Co-embedding Attributed Networks based on code <https://github.com/zfjsail/gae-pytorch> and <https://github.com/mengzaiqiao/CAN> 
+>Zaiqiao Meng, Shangsong Liang, Hongyan Bao, Xiangliang Zhang. Co-embedding Attributed Networks. (WSDM2019)
+
+
+### Requirements
+- Python 3.7.4
+- PyTorch 1.5.0
+- install requirements via 
+
+```
+pip install -r requirements.txt
+``` 
+
+### How to run
+
+```
+python train.py
+```
+
+
+#### Facebook dataset with the default parameter settings
+
+```
+Epoch: 0194 train_loss= 0.75375 log_lik= 0.69382 KL= 0.05993 train_acc= 0.73382 val_edge_roc= 0.98516 val_edge_ap= 0.98455 val_attr_roc= 0.95473 val_attr_ap= 0.95721 time= 1.71142
+Epoch: 0195 train_loss= 0.75215 log_lik= 0.69217 KL= 0.05998 train_acc= 0.73484 val_edge_roc= 0.98577 val_edge_ap= 0.98492 val_attr_roc= 0.95465 val_attr_ap= 0.95746 time= 1.64731
+Epoch: 0196 train_loss= 0.75135 log_lik= 0.69133 KL= 0.06002 train_acc= 0.73486 val_edge_roc= 0.98588 val_edge_ap= 0.98486 val_attr_roc= 0.95322 val_attr_ap= 0.95755 time= 1.64199
+Epoch: 0197 train_loss= 0.75140 log_lik= 0.69134 KL= 0.06006 train_acc= 0.73556 val_edge_roc= 0.98545 val_edge_ap= 0.98477 val_attr_roc= 0.95652 val_attr_ap= 0.95914 time= 1.63010
+Epoch: 0198 train_loss= 0.75157 log_lik= 0.69146 KL= 0.06010 train_acc= 0.73477 val_edge_roc= 0.98573 val_edge_ap= 0.98490 val_attr_roc= 0.95497 val_attr_ap= 0.95753 time= 1.65039
+Epoch: 0199 train_loss= 0.75122 log_lik= 0.69107 KL= 0.06015 train_acc= 0.73400 val_edge_roc= 0.98620 val_edge_ap= 0.98523 val_attr_roc= 0.95420 val_attr_ap= 0.95829 time= 1.66717
+Epoch: 0200 train_loss= 0.74931 log_lik= 0.68914 KL= 0.06017 train_acc= 0.73667 val_edge_roc= 0.98601 val_edge_ap= 0.98515 val_attr_roc= 0.95426 val_attr_ap= 0.95744 time= 1.65484
+Optimization Finished!
+Test edge ROC score: 0.9853779088016957
+Test edge AP score: 0.9836879718079673
+Test attr ROC score: 0.9578314765862058
+Test attr AP score: 0.9577498373032282
+```
+
+#### CiteSeer dataset with the default parameter settings  
+```
+Epoch: 0198 train_loss= 0.81845 log_lik= 0.76834 KL= 0.05011 train_acc= 0.66264 val_edge_roc= 0.94756 val_edge_ap= 0.95467 val_attr_roc= 0.92974 val_attr_ap= 0.92059 time= 1.70837
+/Users/storen/anaconda3/lib/python3.7/site-packages/torch/nn/functional.py:1558: UserWarning: nn.functional.tanh is deprecated. Use torch.tanh instead.
+/Users/storen/anaconda3/lib/python3.7/site-packages/torch/nn/functional.py:1558: UserWarning: nn.functional.tanh is deprecated. Use torch.tanh instead.
+Optimization Finished!
+Test edge ROC score: 0.9490130318561492
+Test edge AP score: 0.95856792990438
+Test attr ROC score: 0.9239066109625775
+Test attr AP score: 0.9121636661521142
+```
+
+#### Cora dataset with the default parameter settings 
+```
+Epoch: 0197 train_loss= 0.92730 log_lik= 0.85651 KL= 0.07079 train_acc= 0.64933 val_edge_roc= 0.98261 val_edge_ap= 0.97795 val_attr_roc= 0.89457 val_attr_ap= 0.88565 time= 0.85166  
+Epoch: 0198 train_loss= 0.92594 log_lik= 0.85511 KL= 0.07083 train_acc= 0.65040 val_edge_roc= 0.98230 val_edge_ap= 0.97761 val_attr_roc= 0.89448 val_attr_ap= 0.88571 time= 0.82273
+Epoch: 0199 train_loss= 0.92517 log_lik= 0.85432 KL= 0.07085 train_acc= 0.65058 val_edge_roc= 0.98256 val_edge_ap= 0.97801 val_attr_roc= 0.89523 val_attr_ap= 0.88651 time= 0.81977
+Epoch: 0200 train_loss= 0.92596 log_lik= 0.85508 KL= 0.07088 train_acc= 0.64968 val_edge_roc= 0.98289 val_edge_ap= 0.97837 val_attr_roc= 0.89585 val_attr_ap= 0.88720 time= 0.90153
+Optimization Finished!
+Test edge ROC score: 0.983134251252218
+Test edge AP score: 0.9817151099782778
+Test attr ROC score: 0.895140476178776
+Test attr AP score: 0.8847338611264453
+```

__init__.py

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+
+
+

classification.py

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+from __future__ import division
+from __future__ import print_function
+import numpy as np
+from sklearn.model_selection import train_test_split
+from sklearn.svm import LinearSVC,SVC
+from sklearn.metrics import *
+
+
+def multiclass_node_classification_eval(X, y, ratio=0.5, rnd=2018):
+
+    X_train, X_test, y_train, y_test = train_test_split(
+        X, y, test_size=ratio, random_state=rnd)
+    clf = SVC()
+    clf.fit(X_train, y_train)
+
+    y_pred = clf.predict(X_test)
+
+    macro_f1 = f1_score(y_test, y_pred, average="macro")
+    micro_f1 = f1_score(y_test, y_pred, average="micro")
+
+    return macro_f1, micro_f1
+
+
+def node_classification_F1(Embeddings, y, ratio):
+    macro_f1_avg = 0
+    micro_f1_avg = 0
+    for i in range(10):
+        rnd = np.random.randint(2018)
+        macro_f1, micro_f1 = multiclass_node_classification_eval(
+            Embeddings, y, ratio, rnd)
+        macro_f1_avg += macro_f1
+        micro_f1_avg += micro_f1
+    macro_f1_avg /= 10
+    micro_f1_avg /= 10
+    print ("Macro_f1: " + str(macro_f1_avg))
+    print ("Micro_f1: " + str(micro_f1_avg))
+
+
+def read_label(inputFileName):
+    f = open(inputFileName, "r")
+    lines = f.readlines()
+    f.close()
+    N = len(lines)
+    y = np.zeros(N, dtype=int)
+    i = 0
+    for line in lines:
+        l = line.strip("\n\r")
+        y[i] = int(l)
+        i += 1
+    return y
+
+
+datasets = ['cora' ]#'cora', 'citeseer', 'pubmed', 'pubmed','BlogCatalog']
+for datasetname in datasets:
+    for ratio in [0.2]:
+        print('dataset:', datasetname, ',ratio:', ratio)
+        embedding_node_result_file = "result/AGAE_{}_n_mu.emb.npy".format(datasetname)
+        label_file = "data/" + datasetname + ".label"
+        y = read_label(label_file)
+        Embeddings = np.load(embedding_node_result_file)
+        node_classification_F1(Embeddings, y, ratio)