Neural-Brane: An inductive approach for attributed network embedding

Abstract

Network embedding methodologies, which learn a distributed vector representation for each vertex in a network, have shown to achieve superior performance in many realworld applications, such as node classification, link prediction, and community detection. However, the existing methods for network embedding are unable to generate representation vectors for unseen vertices; besides, these methods only utilize topological information from the network ignoring a rich set of nodal attributes, which is abundant in all real-life networks. In this paper, we present a novel network embedding approach called Neural-Brane, which overcomes both of the above limitations. For a given network, Neural-Brane extracts latent feature representation of its vertices using a designed neural network model that unifies network topological information and nodal attributes. Additionally, Neural-Brane is an inductive embedding approach, which enables generating embedding vectors for unseen future vertices of the attributed network. We evaluate the quality of vertex embedding produced by Neural-Brane by solving the node classification task on four realworld graph datasets. Experimental results demonstrate the superiority of Neural-Brane over the state-of-the-art existing methods.