  Network Embedding is the task of learning continuous node representations for
networks, which has been shown effective in a variety of tasks such as link
prediction and node classification. Most of existing works aim to preserve
different network structures and properties in low-dimensional embedding
vectors, while neglecting the existence of noisy information in many real-world
networks and the overfitting issue in the embedding learning process. Most
recently, generative adversarial networks (GANs) based regularization methods
are exploited to regularize embedding learning process, which can encourage a
global smoothness of embedding vectors. These methods have very complicated
architecture and suffer from the well-recognized non-convergence problem of
GANs. In this paper, we aim to introduce a more succinct and effective local
regularization method, namely adversarial training, to network embedding so as
to achieve model robustness and better generalization performance. Firstly, the
adversarial training method is applied by defining adversarial perturbations in
the embedding space with an adaptive $L_2$ norm constraint that depends on the
connectivity pattern of node pairs. Though effective as a regularizer, it
suffers from the interpretability issue which may hinder its application in
certain real-world scenarios. To improve this strategy, we further propose an
interpretable adversarial training method by enforcing the reconstruction of
the adversarial examples in the discrete graph domain. These two regularization
methods can be applied to many existing embedding models, and we take DeepWalk
as the base model for illustration in the paper. Empirical evaluations in both
link prediction and node classification demonstrate the effectiveness of the
proposed methods.
