Adversarial network embedding using structural similarity
Network embedding which aims to embed a given network into a low dimensional vector space has been proved effective in various network analysis and mining tasks such as node classification, link prediction and network visualization. The emerging network embedding methods have shifted of emphasis in utilizing mature deep learning models. The neural network based network embedding has become a mainstream solution because of its high efficiency and capability of preserving the nonlinear characteristics of the network. In this paper, we propose Adversarial Network Embedding using Structural Similarity (ANESS) a novel, versatile, low complexity GAN based network embedding model which utilizes the inherent vertex to vertex structural similarity attribute of the network. ANESS learns robustness and effective vertex embeddings via a adversarial training procedure. Specifically, our method aims to exploit the strengths of generative adversarial networks in generating high quality samples and utilize the structural similarity identity of vertexes to learn the latent representations of a network. Meanwhile, ANESS can dynamically update the strategy of generating samples during each training iteration. The extensive experiments have been conducted on the several benchmark network datasets, and empirical results demonstrate that ANESS significantly outperforms other state of the art network embedding methods.
Frontiers Comput. Sci.