Extractive convolutional adversarial networks for network embedding

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.