Adversarial Learning on Heterogeneous Information Networks

Network embedding, which aims to represent network data in a low-dimensional space, has been commonly adopted for analyzing heterogeneous information networks (HIN). Although exiting HIN embedding methods have achieved performance improvement to some extent, they still face a few major weaknesses. Most importantly, they usually adopt negative sampling to randomly select nodes from the network, and they do not learn the underlying distribution for more robust embedding. Inspired by generative adversarial networks (GAN), we develop a novel framework HeGAN for HIN embedding, which trains both a discriminator and a generator in a minimax game. Compared to existing HIN embedding methods, our generator would learn the node distribution to generate better negative samples. Compared to GANs on homogeneous networks, our discriminator and generator are designed to be relation-aware in order to capture the rich semantics on HINs. Furthermore, towards more effective and efficient sampling, we propose a generalized generator, which samples "latent" nodes directly from a continuous distribution, not confined to the nodes in the original network as existing methods are. Finally, we conduct extensive experiments on four real-world datasets. Results show that we consistently and significantly outperform state-of-the-art baselines across all datasets and tasks.