Neural Brane: Neural Bayesian Personalized Ranking for Attributed Network Embedding
Network embedding methodologies, which learn a distributed vector representation for each vertex in a network, have attracted considerable interest in recent years. Existing works have demonstrated that vertex representation learned through an embedding method provides superior performance in many real world applications, such as node classification, link prediction, and community detection. However, most of the existing methods for network embedding only utilize topological information of a vertex, ignoring a rich set of nodal attributes (such as user profiles of an online social network, or textual contents of a citation network) which is abundant in all real life networks. A joint network embedding that takes into account both attributional and relational information entails a complete network information and could further enrich the learned vector representations. In this work, we present Neural Brane, a novel Neural Bayesian Personalized Ranking based Attributed Network Embedding. 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. Besides, it utilizes Bayesian personalized ranking objective, which exploits the proximity ordering between a similar node pair and a dissimilar node pair. We evaluate the quality of vertex embedding produced by Neural Brane by solving the node classification and clustering tasks on four real world datasets. Experimental results demonstrate the superiority of our proposed method over the state of the art existing methods.
Data Science and Engineering