  Learning low-dimensional representations of networks has proved effective in
a variety of tasks such as node classification, link prediction and network
visualization. Existing methods can effectively encode different structural
properties into the representations, such as neighborhood connectivity
patterns, global structural role similarities and other high-order proximities.
However, except for objectives to capture network structural properties, most
of them suffer from lack of additional constraints for enhancing the robustness
of representations. In this paper, we aim to exploit the strengths of
generative adversarial networks in capturing latent features, and investigate
its contribution in learning stable and robust graph representations.
Specifically, we propose an Adversarial Network Embedding (ANE) framework,
which leverages the adversarial learning principle to regularize the
representation learning. It consists of two components, i.e., a structure
preserving component and an adversarial learning component. The former
component aims to capture network structural properties, while the latter
contributes to learning robust representations by matching the posterior
distribution of the latent representations to given priors. As shown by the
empirical results, our method is competitive with or superior to
state-of-the-art approaches on benchmark network embedding tasks.
