Adversarial network embedding on heterogeneous information networks

Abstract

Network embedding has been proven to be helpful for solving real-world problems. Moreover, real-world networks are often heterogeneous information networks(HINs). In this paper, we propose a new adversarial framework for heterogeneous network embedding, namely AGNE-HIN. AGNE-HIN can learn latent code distribution in the network through a generative adversarial way. What’s more, to reduce the global smoothness of the embedded vector caused by GAN, we apply perturbation to the input to form adversarial data. Experimental results verify our design and demonstrate the effectiveness of the proposed method in node clustering, link prediction and similarity ranking tasks.