A simple approach to attributed graph embedding via enhanced autoencoder

Network Representation Learning (NRL) aims at learning a low-dimensional representation of nodes in a graph such that its properties are preserved in the learned embedding. NRL methods may exploit different sources of information such as the structural or attribute information of the graph. Recent efforts have shown that jointly using both structure and attributes helps in learning a better representation. Most of these methods rely on highly complex procedures, such as sampling, which makes them non-scalable to large graphs. In this paper, we propose a simple and scalable deep neural network model that learns an embedding by jointly incorporating the network structure and the attribute information. Specifically, the model employs an enhanced decoder that preserves global network structure and also handles the non-linearities of both the network structure and network attributes. We discuss node classification, link prediction, and network reconstruction experiments on four real-world d...