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README.md

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+## CAN: Co-embedding Attributed Networks
+This repository contains the Python implementation for CAN
+> Zaiqiao Meng, Shangsong Liang, Hongyan Bao, Xiangliang Zhang. Co-embedding Attributed Networks. (WSDM2019)
+Further details about CAN can be found in our paper.
+
+## Requirements
+=================
+* TensorFlow (1.0 or later)
+* python 2.7/3.6
+* scikit-learn
+* scipy
+
+## Run the demo
+=================
+```bash
+python train.py
+```

__init__.py

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+from __future__ import print_function
+from __future__ import division

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)

data/DBLP.attidx

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+#Attributes	172
+0	NSDI
+1	SDM
+2	NDSS
+3	CVPR
+4	COGSCI
+5	UIST
+6	ICSOC
+7	COMPGEOM
+8	CLOUD
+9	POPL
+10	DCC
+11	SC
+12	PPOPP
+13	ASPLOS
+14	USENIX
+15	RE
+16	ICCBR
+17	CONCUR
+18	ICSE
+19	ITC
+20	ICDT
+21	SAS
+22	ICNP
+23	NIPS
+24	IPPS
+25	PG
+26	KDD
+27	EDBT
+28	CHI
+29	VISSYM
+30	AIPS
+31	PKDD
+32	CIDR
+33	HPDC
+34	VR
+35	SIGGRAPH
+36	MM
+37	STOC
+38	AAAI
+39	SI3D
+40	CODES
+41	EUROSYS
+42	ETAPS
+43	SECON
+44	CRYPTO
+45	ICS
+46	ICMCS
+47	SIGCOMM
+48	SCA
+49	HUC
+50	ICPP
+51	SP
+52	IJCAI
+53	ICFP
+54	UAI
+55	PERCOM
+56	VLDB
+57	BIBM
+58	SIGMOD
+59	SIGMETRICS
+60	PPSN
+61	MOBICOM
+62	IPSN
+63	ECAI
+64	IWQOS
+65	IUI
+66	EMSOFT
+67	ESORICS
+68	ICRA
+69	SMA
+70	CCS
+71	COCO
+72	OOPSLA
+73	EUROCRYPT
+74	ICWS
+75	EMNLP
+76	ECCV
+77	SGP
+78	ESA
+79	ICCD
+80	SODA
+81	HOTOS
+82	SEMWEB
+83	RT
+84	SOSP
+85	ICSM
+86	FAST
+87	MODELS
+88	WSDM
+89	DATE
+90	CSCW
+91	ICASSP
+92	HIPEAC
+93	ECOOP
+94	RECOMB
+95	PODS
+96	SENSYS
+97	ACSAC
+98	RAID
+99	MOBIHOC
+100	IWPC
+101	ISSRE
+102	CSFW
+103	RTSS
+104	ASIACRYPT
+105	CNHPCA
+106	SRDS
+107	ACL
+108	CGO
+109	PLDI
+110	NOSSDAV
+111	VEE
+112	ECSCW
+113	VISUALIZATION
+114	FOCS
+115	MSS
+116	CIKM
+117	KBSE
+118	FSE
+119	ECML
+120	IMC
+121	SIGIR
+122	LICS
+123	RTAS
+124	ICDE
+125	CONEXT
+126	ICDM
+127	DAC
+128	OSDI
+129	WWW
+130	ISSTA
+131	FPGA
+132	ICCV
+133	GROUP
+134	COLING
+135	ISCA
+136	FM
+137	USS
+138	MICRO
+139	ICML
+140	MOBISYS
+141	ATAL
+142	WCRE
+143	CP
+144	DSN
+145	HYBRID
+146	MHCI
+147	ESEM
+148	CAV
+149	TCC
+150	KR
+151	MIR
+152	ICCAD
+153	IEEEPACT
+154	CAISE
+155	ICDCS
+156	CHES
+157	ICALP
+158	TABLETOP
+159	MIDDLEWARE
+160	CADE
+161	VMCAI
+162	COLT
+163	EUROGRAPHICS
+164	INFOCOM
+165	PKC
+166	SPAA
+167	DASFAA
+168	LCTRTS
+169	SIGSOFT
+170	PODC
+171	LISA