Global citation recommendation employing generative adversarial network

•We construct a Heterogeneous Bibliographic Network, which exploits semantics among the network objects.•We propose a GAN-based network embedding model to address the network sparsity problem.•We propose a citation recommendation model to produce personalized results corresponding to researchers' preferences.•We conduct extensive experiments on two real-world datasets and prove the significance of the proposed model.