  Network Embedding aims to learn a function mapping the nodes to Euclidean
space contribute to multiple learning analysis tasks on networks. However, the
noisy information behind the real-world networks and the overfitting problem
both negatively impact the quality of embedding vectors. To tackle these
problems, researchers utilize Adversarial Training for Network Embedding
(AdvTNE) and achieve state-of-the-art performance. Unlike the mainstream
methods introducing perturbations on the network structure or the data feature,
AdvTNE directly perturbs the model parameters, which provides a new chance to
understand the mechanism behind. In this paper, we explain AdvTNE theoretically
from an optimization perspective. Considering the Power-law property of
networks and the optimization objective, we analyze the reason for its
excellent results. According to the above analysis, we propose a new activation
to enhance the performance of AdvTNE. We conduct extensive experiments on four
real networks to validate the effectiveness of our method in node
classification and link prediction. The results demonstrate that our method is
superior to the state-of-the-art baseline methods.
