Abstract: Heterogeneous information networks contain rich structural and semantic information. It is a hot topic of current research to retain the structural and semantic information of heterogeneous information networks through network representation learning. Traditional heterogeneous information network representation learning methods are limited to preserving semantic information in heterogeneous information networks in the form of meta-paths, which is lack of considering the distribution of all nodes in the network, and the retained information is insufficient. Therefore, this paper proposes a heterogeneous information network representation learning method (HINGAN) based on a generative adversarial networks (GAN), which can better retain the information in the network and improve the robustness of representation learning. First of all, the GAN generation model generates fake data that retains network information, then samples the real data from the Gaussian distribution, and finally sends the real and fake data into the GAN discriminant model at the same time. Through the game of the generated model and the discriminant model, the node representation with more information is obtained. The experimental results based on two real data sets show that the proposed model is better than the traditional heterogeneous information network method in node classification and link prediction tasks.

Key words: network representation learning, heterogeneous information networks, generative adversarial networks