Adversarial Network Embedding

Understand

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.