  Node embeddings have become an ubiquitous technique for representing graph
data in a low dimensional space. Graph autoencoders, as one of the widely
adapted deep models, have been proposed to learn graph embeddings in an
unsupervised way by minimizing the reconstruction error for the graph data.
However, its reconstruction loss ignores the distribution of the latent
representation, and thus leading to inferior embeddings. To mitigate this
problem, we propose a random walk based method to regularize the
representations learnt by the encoder. We show that the proposed novel
enhancement beats the existing state-of-the-art models by a large margin (upto
7.5\%) for node clustering task, and achieves state-of-the-art accuracy on the
link prediction task for three standard datasets, cora, citeseer and pubmed.
Code available at https://github.com/MysteryVaibhav/DW-GAE.
