Network representation learning (also known as Graph embedding) is a technique to map the nodes of a network to a lower dimensional vector space. Random walk based representation techniques are found to be efficient as they can easily preserve different orders of proximities between the nodes in the embedding space. Most of the social networks now-a-days have some content (or attributes) associated with each node. These attributes can provide complementary information along with the link structure of the network. But in a real life network, the information carried by the link structure and that by the attributes vary significantly over the nodes. Most of the existing unsupervised attributed network embedding algorithms do not distinguish between the link structure and the attributes of a node depending on their informativeness.

In this work, we propose an unsupervised node embedding technique that exploits both the structure and attributes by intelligently prioritizing one of them, in the random walk, for each node separately. We convert the network into a multi-layered graph and propose a novel random walk based on the informativeness of a node in different layers. This unified approach is simple and computationally fast, yet able to use the content as a complement to structure and viceversa. Experimental evaluations on four real world publicly available datasets show the merit of our approach (up to 168.75% improvement) compared to the state-of-the-art algorithms in the domain. We make the source code available to download.