| Summary: | Abstract Graph representation learning has attracted increasing attention in a variety of applications that involve learning on non‐Euclidean data. Recently, generative adversarial networks(GAN) have been increasingly applied to the field of graph representation learning, and large progress has been made. However, most GAN‐based graph representation learning methods use adversarial learning strategies directly on the update of the vector representation instead of the deep embedding mechanism. Compared with deep models, these methods are less capable of learning nonlinear features. To address this problem and take full advantage of the essential advantages of GAN, we propose to use adversarial idea on the reconstruction mechanism of deep autoencoders. Specifically, the generator and the discriminator are the two basic components of the GAN structure. We use the deep autoencoder as the discriminator, which can capture the highly nonlinear structure of the graph. In addition, the generator another generative model is introduced into the adversarial learning system as a competitor. A series of empirical results proved the effectiveness of the new approach.
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