TY - JOUR
T1 - Extractive convolutional adversarial networks for network embedding
AU - Qin, Xiaorui
AU - Rao, Yanghui
AU - Xie, Haoran
AU - Yin, Jian
AU - Wang, Fu Lee
N1 - Publisher Copyright:
© 2019, Springer Science+Business Media, LLC, part of Springer Nature.
PY - 2020/5/1
Y1 - 2020/5/1
N2 - Network embedding plays an important role in various real-world applications. Most traditional algorithms focus on the topological structure while ignore the information from node attributes. The attributed information is potentially valuable to network embedding. To solve this problem, we propose a deep learning model named Extractive Convolutional Adversarial Network (ECAN) for network embedding. This model aims to extract the latent representations from the topological structure, the attributed information, and labels via three components. In the first part, ECAN extracts features from the topological structure and the attributed information of nodes separately. The second part is a prediction model, which aims to exploit labels of vertices. The third part is a convolutional adversarial model. We train it to distinguish the extractive features which are generated by the hidden layers in the extractive network from either the attributed information or the topological structure. Experiments on six real-world datasets demonstrate the effectiveness of ECAN when compared with state-of-the-art embedding algorithms.
AB - Network embedding plays an important role in various real-world applications. Most traditional algorithms focus on the topological structure while ignore the information from node attributes. The attributed information is potentially valuable to network embedding. To solve this problem, we propose a deep learning model named Extractive Convolutional Adversarial Network (ECAN) for network embedding. This model aims to extract the latent representations from the topological structure, the attributed information, and labels via three components. In the first part, ECAN extracts features from the topological structure and the attributed information of nodes separately. The second part is a prediction model, which aims to exploit labels of vertices. The third part is a convolutional adversarial model. We train it to distinguish the extractive features which are generated by the hidden layers in the extractive network from either the attributed information or the topological structure. Experiments on six real-world datasets demonstrate the effectiveness of ECAN when compared with state-of-the-art embedding algorithms.
KW - Attributed network
KW - Convolutional neural network
KW - Generative adversarial network
KW - Network embedding
UR - http://www.scopus.com/inward/record.url?scp=85075356200&partnerID=8YFLogxK
U2 - 10.1007/s11280-019-00740-7
DO - 10.1007/s11280-019-00740-7
M3 - Article
AN - SCOPUS:85075356200
SN - 1386-145X
VL - 23
SP - 1925
EP - 1944
JO - World Wide Web
JF - World Wide Web
IS - 3
ER -