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Network embedding methodologies, which learn a distributed vectorrepresentation for each vertex in a network, have attracted considerableinterest in recent years. Existing works have demonstrated that vertexrepresentation learned through an embedding method provides superiorperformance in many real-world applications, such as node classification, linkprediction, and community detection. However, most of the existing methods fornetwork embedding only utilize topological information of a vertex, ignoring arich set of nodal attributes (such as, user profiles of an online socialnetwork, or textual contents of a citation network), which is abundant in allreal-life networks. A joint network embedding that takes into account bothattributional and relational information entails a complete network informationand could further enrich the learned vector representations. In this work, wepresent Neural-Brane, a novel Neural Bayesian Personalized Ranking basedAttributed Network Embedding. For a given network, Neural-Brane extracts latentfeature representation of its vertices using a designed neural network modelthat unifies network topological information and nodal attributes; Besides, itutilizes Bayesian personalized ranking objective, which exploits the proximityordering between a similar node-pair and a dissimilar node-pair. We evaluatethe quality of vertex embedding produced by Neural-Brane by solving the nodeclassification and clustering tasks on four real-world datasets. Experimentalresults demonstrate the superiority of our proposed method over thestate-of-the-art existing methods.

Neural-Brane: Neural Bayesian Personalized Ranking for Attributed Network Embedding