Network alignment plays an important role in a variety of applications. Many traditional methods explicitly or implicitly assume the alignment consistency which might suffer from over-smoothness, whereas some recent embedding based methods could somewhat embrace the alignment disparity by sampling negative alignment pairs. However, under different or even competing designs of negative sampling distributions, some methods advocate positive correlation which could result in false negative samples incorrectly violating the alignment consistency, whereas others champion negative correlation or uniform distribution to sample nodes which may contribute little to learning meaningful embeddings. In this paper, we demystify the intrinsic relationships behind various network alignment methods and between these competing design principles of sampling. Specifically, in terms of model design, we theoretically reveal the close connections between a special graph convolutional network model and the traditional consistency based alignment method. For model training, we quantify the risk of embedding learning for network alignment with respect to the sampling distributions. Based on these, we propose NeXtAlign which strikes a balance between alignment consistency and disparity. We conduct extensive experiments that demonstrate the proposed method achieves significant improvements over the state-of-the-arts.